Search results for: heart sounds

Authors: Fatima Mokeddem

Abstract:

The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.

Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Omer Abdalla Ishag, Magdi Baker Amien

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Physicians used the stethoscope for listening patient heart sounds in order to make a diagnosis. However, the determination of heart conditions by acoustic stethoscope is a difficult task so it requires special training of medical staff. This study developed an accurate model for analyzing the phonocardiograph signal based on PC and DSP processor. The system has been realized into two phases; offline and real time phase. In offline phase, 30 cases of heart sounds files were collected from medical students and doctor's world website. For experimental phase (real time), an electronic stethoscope has been designed, implemented and recorded signals from 30 volunteers, 17 were normal cases and 13 were various pathologies cases, these acquired 30 signals were preprocessed using an adaptive filter to remove lung sounds. The background noise has been removed from both offline and real data, using wavelet transform, then graphical and statistics features vector elements were extracted, finally a look-up table was used for classification heart sounds cases. The obtained results of the implemented system showed accuracy of 90%, 80% and sensitivity of 87.5%, 82.4% for offline data, and real data respectively. The whole system has been designed on TMS320VC5509a DSP Platform.

Keywords: code composer studio, heart sounds, phonocardiograph, wavelet transform

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

Abstract:

Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Ali Mohammed Saleh Al-Hamzi

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This study aims at finding out the most difficult position in pronouncing the bilabial plosive sounds at the fourth level of English foreign language students of the Faculty of Education, Mahweet, Sana’a University in Yemen. The subject of this study were 50 participants from English foreign language students aged 22-25. In describing sounds according to their place of articulation, sounds are classified as bilabial, labiodental, dental, alveolar, post-alveolar, palato-alveolar retroflex, palatal, velar, uvular, and glottal. In much the same way, sounds can be described in their manner of articulation as plosives, nasals, affricates, flaps, taps, rolls, fricatives, laterals, frictionless continuants, and semi-vowels. For English foreign language students in Yemen, there are some articulators that are difficult to pronounce. In this study, the researcher focuses on difficulties in pronouncing the English bilabial plosive sounds among English foreign language students. It can be in the initial, medial, and final positions. The problem discussed in this study was: which position is the most difficult in pronouncing the English bilabial plosive sounds? To solve the problem, a descriptive qualitative method was conducted in this study. The data were collected from each English bilabial plosive sounds produced by students. Finally, the researcher reached that the most difficult position in pronouncing the English bilabial plosive sounds is when English bilabial plosive /p/ and /b/ occur word-finally, where both are voiceless.

Keywords: difficulty, EFL students’ pronunciation, bilabial sounds, plosive sounds

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Authors: Hosein Almasikia

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Sarableh City is located in the west of Iran and in the seismic zone of Zagros. After the Azgole-Sarpol Zahab earthquake with a magnitude of 3.7 Richter on November 21, 2016, in some parts of Sarableh city, horrible sounds were heard by people. There is also a sound similar to the wear of the mill by some of the residents. Vibration studies and field investigations showed that these sounds have a geological origin and are emitted from the ground to the surface and may be related to geological hazards such as landslides, collapse of karstic zones, etc. In this study, an attempt has been made to investigate the possible relationship between these abnormal sounds and geological hazards.

Keywords: Sarable, Zagros, landslide, karstic zone

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Authors: Adam Rosiński

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This paper discusses an experiment conducted with two groups of participants, composed of musicians and non-musicians, in order to investigate the impact of the speed of a sound sequence and the order of sounds on the grouping of sounds into sequences of six tones. Significant differences were observed between musicians and non-musicians with respect to the threshold sequence speed at which the sequence was split into two streams. The differences in the results for the two groups suggest that the musical education of the participating listeners may be a vital factor. The criterion of musical education should be taken into account during experiments so that the results obtained are reliable, uniform, and free from interpretive errors.

Keywords: auditory scene analysis, education, hearing, psychoacoustics

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Authors: K. Doi, T. Nishimura, M. Umeda

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There are very few sound interfaces that both healthy people and hearing handicapped people can use to play together. In this study, we developed a sound tactile interface that makes use of the human sensation of stiffness. The interface comprises eight elastic objects having varying degrees of stiffness. Each elastic object is shaped like a column. When people with and without hearing disabilities press each elastic object, different sounds are produced depending on the stiffness of the elastic object. The types of sounds used were “Do Re Mi sounds.” The interface has a major advantage in that people with or without hearing disabilities can play with it. We found that users were able to recognize the hardness sensation and relate it to the corresponding Do Re Mi sounds.

Keywords: tactile sense, sound interface, stiffness perception, elastic object

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Authors: Razieh Khalafi

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Myocardial infarction (MI) or acute myocardial infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analyzing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behavior of these signals were checked. Results shows this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

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Authors: Razi Khalafi

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Myocardial Infarction (MI) or acute Myocardial Infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analysing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behaviour of these signals were checked. Results show this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

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Authors: Aw Adi Arryansyah

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Using electronic stethoscope for detecting heartbeat sound, and breath sounds, are the effective way to investigate cardiovascular diseases. On the other side, technology is growing towards mobile. Almost everyone has a smartphone. Smartphone has many platforms. Creating mobile applications also became easier. We also can use HTML5 technology to creating mobile apps. Android is the most widely used type. This is the reason for us to make a wireless electronic stethoscope based on Android mobile. Android based Wireless Electronic Stethoscope designed by a simple system, uses sound sensors mounted membrane, then connected with Bluetooth module which will send the heart auscultation voice input data by Bluetooth signal to an android platform. On the software side, android will read the voice input then it will translate to beautiful visualization and release the voice output which can be regulated about how much of it is going to be released. We can change the heart beat sound into BPM data, and heart beat analysis, like normal beat, bradycardia or tachycardia.

Keywords: wireless, HTML 5, auscultation, bradycardia, tachycardia

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Mariam Aref Mahmoud

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After centuries of historical development, Egypt is no stranger to national identity frustrations. What may or may not be counted as this “national identity” becomes a source of contention. Today, after decades of neoliberal reform, Cairo has become the center of Egypt’s cultural debacle. At its heart, the Egyptian capital serves as Egypt’s extension into global capitalism, its flailing hope to become part of the modernized, cosmopolitan world. Yet, to converge into this image of cosmopolitanism, Cairo must silence the perceived un-modernized sounds, cultures, and spaces that arise from within its alleyways. Currently, the agitation surrounding shaabi music, particularly, that of mahraganat, places these contentions to the center of the modernization debates. This paper will discuss the process through which the conversations between modernization, space, and culture have taken place through a historical analysis of national identity formation under Egypt’s neoliberal regimes. Through this, the paper concludes that music becomes a spatial force through which public space, identity, and globalization must be contested. From these findings researchers can then analyze Cairo through not only its physical landscapes, but also its metaphysical features – such as the soundscape.

Keywords: music, space, globalization, Cairo

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Authors: Dongjoo Kim, Chang-Sik Son, Won-Seok Kang

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Adequate sleep affects various parts of one’s overall physical and mental life. As one of the methods in determining the appropriate amount of sleep, this research presents a heart rate based sleep quality index. In order to evaluate sleep quality using the heart rate, sleep data from 280 subjects taken over one month are used. Their sleep data are categorized by a three-part heart rate range. After categorizing, some features are extracted, and the statistical significances are verified for these features. The results show that some features of this sleep quality index model have statistical significance. Thus, this heart rate based sleep quality index may be a useful discriminator of sleep.

Keywords: sleep, sleep quality, heart rate, statistical analysis

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Authors: Mahmudjon Kuchkarov, Marufjon Kuchkarov

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The origin of ‘Human Language’ is still a secret and the most interesting subject of historical linguistics. The core element is the nature of labeling or coding the things or processes with symbols and sounds. In this paper, we investigate human’s involuntary Paired Sounds and Shape Production (PSSP) and its contribution to the development of early human communication. Aimed at twenty-six volunteers who provided many physical movements with various difficulties, the research team investigated the natural, repeatable, and paired sounds and shape productions during human activities. The paper claims the involvement of Paired Sounds and Shape Production (PSSP) in the phonetic origin of some modern words and the existence of similarities between elements of PSSP with characters of the classic Latin alphabet. The results may be used not only as a supporting idea for existing theories but to create a closer look at some fundamental nature of the origin of the languages as well.

Keywords: body shape, body language, coding, Latin alphabet, merging method, movement language, movement sound, natural sound, origin of language, pairing, phonetics, sound and shape production, word origin, word semantic

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Authors: Ihab Elaff

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Simulating the heart’s electrical stimulation is essential in modeling and evaluating the electrophysiology behavior of the heart. For achieving that, there are two structures in concern: the ventricles’ Myocardium, and the ventricles’ Conduction Network. Ventricles’ Myocardium has been modeled as anisotropic material from Diffusion Tensor Imaging (DTI) scan, and the Conduction Network has been extracted from DTI as a case-based structure based on the biological properties of the heart tissues and the working methodology of the Magnetic Resonance Imaging (MRI) scanner. Results of the produced activation were much similar to real measurements of the reference model that was presented in the literature.

Keywords: diffusion tensor, DTI, heart, conduction network, excitation propagation

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Authors: Leila Kargar, Homa Abri, Golsa Safai

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The occurrence of chest pain among geriatrics could be considered as a predictor of heart issues. There is a need for attention to this pain among this population. This review paper has tried to collect the recent data with attention to the chest pain among geriatrics. This review paper has focused on specific keywords, including chest pain, heart issues, and geriatrics, among published papers from 2015 till 2020. To collect data for this purpose, Scopus, Web of Sciences, and PubMed were used. After inserting related papers to the Endnote, an independent researcher checked the abstract, and papers with unclear methods or non-English language were excluded. Finally, 7-papers were included in this review paper. The findings of those papers showed that chest pain could be a predictor for heart issues, and also, there is a direct relationship between chest pain and heart issues among geriatrics. So, early detection and an accurate decision could be helpful to prevent heart issues in this population.

Keywords: pain, heart issue, geriatrics, health

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Authors: M. Žibert, T. Špindler, S. Uhan, A. Lisec

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We can find Slovenia in the heart of Europe and has really good geographical location. With same slogan are promoted Switzerland, Montenegro, Greece and probably many others. However, from anatomic point of view, injustice is being made to someone because the heart is placed only in left part of chest cavity and there we can`t find place for the entire territory from Switzerland to the south of Balkan.

Keywords: Ljubljana, logistics, Slovenia, tourism

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Authors: C. C .D. Kulathilake, M. Jayatilake, T. Takahashi

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The myocardium is composed of specialized muscle which relies mainly on fatty acid and sugar metabolism and it is widely contribute to the heart functioning. The changes of the cardiac energy-producing system during heart failure have been proved using autoradiography techniques. This study focused on evaluating sugar and fatty acid metabolism in myocardium as cardiac energy getting system using heart-accumulated radiopharmaceuticals. Two sets of autoradiographs of heart cross sections of Lewis male rats were analyzed and the time- accumulation curve obtained with use of the MATLAB image processing software to evaluate fatty acid and sugar metabolic functions.

Keywords: autoradiographs, fatty acid, radiopharmaceuticals, sugar

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Authors: C. Bala Prashanth Reddy, B. Abhinay, C. Sreekar, D. V. Shobhana Priscilla

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The project aims in designing an emergency pacemaker which is capable of giving shocks to a human heart which has stopped working suddenly. A pacemaker is a machine commonly used by cardiologists. This machine is used in order to shock a human’s heart back into usage. The way the heart works is that there are small cells called pacemakers sending electrical pulses to cardiac muscles that tell the heart when to pump blood. When these electrical pulses stop, the heart stops beating. When this happens, a pacemaker is used to shock the heart muscles and the pacemakers back into action. The way this is achieved is by rubbing the two panels of the pacemaker together to create an adequate electrical current, and then the heart gets back to the normal state. The project aims in designing a system which is capable of continuously displaying the heart beat and blood pressure of a person on LCD. The concerned doctor gets the heart beat and also the blood pressure details continuously through the GSM Modem in the form of SMS alerts. In case of abnormal condition, the doctor sends message format regarding the amount of electric shock needed. Automatically the microcontroller gives the input to the pacemaker which in turn gives the shock to the patient. Heart beat monitor and display system is a portable and a best replacement for the old model stethoscope which is less efficient. The heart beat rate is calculated manually using stethoscope where the probability of error is high because the heart beat rate lies in the range of 70 to 90 per minute whose occurrence is less than 1 sec, so this device can be considered as a very good alternative instead of a stethoscope.

Keywords: missing R wave, PWM, demand pacemaker, heart

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Authors: M. Sayyour, M. Abdulkareem, O. Osman, S. Salmeh

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Emphatic sounds in Arabic are /tˤ/, /sˤ/, /dˤ/, and /ðˤ/. They involve a secondary articulation in the pharynx area as opposed to their counterparts: /t/,/s/,/d/and /ð/. Although they are present in most Arabic dialects, some dialects have lost this class as a historical development, such as Maltese Arabic. It has been found that there is a difference in the pronunciation of these emphatic sounds between the two genders, arguing that males tend to produce more evident emphasis than females. This study builds on these studies by trying to investigate whether listeners perceive fully emphatic sounds as more masculine and less emphatic sounds as more feminine. Furthermore, the study aims to find out which is more important in this perception process: the emphatic consonant itself or the vowel following it. To test this, natural and manipulated tokens of two male and two female speakers were used. The natural tokens include words that have emphatic consonant and emphatic vowel and tokens that have plain consonant and plain vowel. The manipulated tokens include words that have emphatic consonant but central vowel and plain consonant followed by the same central vowel. These manipulated tokens allow us to see whether the consonant will still affect the perception even if the vowel is controlled. Another group of words that contained no emphatic sounds was used as a control group. The total number of tokens (natural, manipulated, and control) are 160 tokens. After that, 60 university students (30 males and 30 females) listened to these tokens and responded by choosing a specific character that they think is likely to produce each token. The characters’ descriptions are carefully written with two degrees of femininity and two degrees of masculinity. The preliminary results for the femininity level showed that the highest degree of femininity was for tokens that contain a plain consonant and a plain vowel. The lowest level of femininity was given for tokens that have fully emphatic consonant and vowel. For the manipulated tokens that contained plain consonant and central vowel, the femininity degree was high which indicates that the consonant is more important than the vowel, while for the manipulated tokens that contain emphatic consonant and a central vowel, the femininity level was higher than that for the tokens that have emphatic consonant and emphatic vowel, which indicates that the vowel is more important for the perception of emphatic consonants. These results are interpreted in light of feminist linguistic theories, linguistic expectations, performed gender and linguistic change theories.

Keywords: Emphatic sounds, gender studies, perception, sociophonetics

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Authors: Mazhar B. Tayel, Eslam I. AlSaba

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The heart is the most important part in any body organisms. It effects and affected by any factor in the body. Therefore, it is a good detector of any matter in the body. When the heart signal is non-stationary signal, therefore, it should be study its variability. So, the Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and have become important dependent measure in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability. However, remain complex issues are fraught with pitfalls. This paper presents one of the non-linear techniques to analyze HRV. It discusses 'What Poincare plot is?', 'How it is work?', 'its usage benefits especially in HRV', 'the limitation of Poincare cause of standard deviation SD1, SD2', and 'How overcome this limitation by using complex correlation measure (CCM)'. The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2.

Keywords: heart rate variability, chaotic system, poincare, variance, standard deviation, complex correlation measure

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Authors: Nazanin Ahmadi Daryakenari

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Background: Diabetes significantly influences the risk of heart failure. The interplay between distinct types of diabetes, heart failure, and their distribution across various age groups remains an area of active exploration. This study endeavors to scrutinize the age group distribution in publications addressing Type 1 and Type 2 diabetes and heart failure on PubMed while also examining the evolving publication trends. Methods: We leveraged E-utilities and RegEx to search and extract publication data from PubMed using various mesh terms. Subsequently, we conducted descriptive statistics and t-tests to discern the differences between the two diabetes types and the distribution across age groups. Finally, we analyzed the temporal trends of publications concerning both types of diabetes and heart failure. Results: Our findings revealed a divergence in the age group distribution between Type 1 and Type 2 diabetes within heart failure publications. Publications discussing Type 2 diabetes and heart failure were more predominant among older age groups, whereas those addressing Type 1 diabetes and heart failure displayed a more balanced distribution across all age groups. The t-test revealed no significant difference in the means between the two diabetes types. However, the number of publications exploring the relationship between Type 2 diabetes and heart failure has seen a steady increase over time, suggesting an escalating interest in this area. Conclusion: The dissection of publication patterns on PubMed uncovers a pronounced association between Type 2 diabetes and heart failure within older age groups. This highlights the critical need to comprehend the distinct age group differences when examining diabetes and heart failure to inform and refine targeted prevention and treatment strategies.

Keywords: Type 1 diabetes, Type 2 diabetes, heart failure, age groups, publication patterns, PubMed

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Authors: Wasila Lawan Gadanya

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This paper examines the problems of learning English vowel pronunciation. The objective is to identify some of the factors that affect the learning of English vowel sounds and their proper realization in words. The theoretical framework adopted is based on both error analysis and contrastive analysis. The data collection instruments used in the study are questionnaire and word list for the respondents (students) and observation of some of their lecturers. All the data collected were analyzed using simple percentage. The findings show that it is not a single factor that affects the learning of English vowel pronunciation rather many factors concurrently do so. Among the factors examined, it has been found that lack of correlation between English orthography and its pronunciation, not mother-tongue (which most people consider as a factor affecting learning of the pronunciation of a second language), has the greatest influence on students’ learning and realization of English vowel sounds since the respondents in this study are from different ethnic groups of Nigeria and thus speak different languages but having the same or almost the same problem when pronouncing the English vowel sounds.

Keywords: English vowels, learning, Nigeria, pronunciation

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Emilie Zimet

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During our department meetings for teachers of children with learning disabilities and difficulties, we often discuss the best practices for supporting students who come to school with literacy difficulties. After completing Sounds Write and Writing Revolution courses, it seems there is a possibility to link approaches and still maintain fidelity to a program and provide individualised instruction to support students with such difficulties and disabilities. In this case study, the researcher has been focussing on how best to use the knowledge acquired to provide quality intervention that targets the varied areas of challenge that students require support in. Students present to school with a variety of co-occurring reading and writing deficits and with complementary approaches, such as The Writing Revolution and Sounds Write, it is possible to support students to improve their fundamental skills in these key areas. Over the next twelve weeks, the researcher will collect data on current students with whom this approach will be trialled and then compare growth with students from last year who received support using Sounds-Write only. Maintaining fidelity may be a potential challenge as each approach has been tested in a specific format for best results. The aim of this study is to determine if approaches can be combined, so the implementation will need to incorporate elements of both reading (from Sounds Write) and writing (from The Writing Revolution). A further challenge is the time length of each session (25 minutes), so the researcher will need to be creative in the use of time to ensure both writing and reading are targeted while ensuring the programs are implemented. The implementation will be documented using student work samples and planning documents. This work will include a display of findings using student learning samples to demonstrate the importance of co-targeting the reading and writing challenges students come to school with.

Keywords: literacy difficulties, intervention, individual differences, methods of provision

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: B. Kishore Kumar, Pogula Rakesh, T. Kishore Kumar

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The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB.

Keywords: percussive sounds, spectral centroid, spectral energy, silence removal, feature extraction

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Authors: Yongrong Liu, Xin Tang

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Background: Persistent atrial fibrillation is a common arrhythmia closely related to heart failure. Heart rate control is an essential strategy for treating persistent atrial fibrillation. Still, the understanding of the relationship between different heart rate control levels and the risk of heart failure rehospitalization is limited. Objective: The objective of the study is to determine the relationship between different levels of heart rate control in patients with persistent atrial fibrillation and the risk of readmission for heart failure. Methods: We conducted a retrospective dual-centre cohort study, collecting data from patients with persistent atrial fibrillation who received outpatient treatment at two tertiary hospitals in central and western China from March 2019 to March 2020. The collected data included age, gender, body mass index (BMI), medical history, and hospitalization frequency due to heart failure. Patients were divided into three groups based on their heart rate control levels: Group I with a resting heart rate of less than 80 beats per minute, Group II with a resting heart rate between 80 and 100 beats per minute, and Group III with a resting heart rate greater than 100 beats per minute. The readmission rates due to heart failure within one year after discharge were statistically analyzed using propensity score matching in a 1:1 ratio. Differences in readmission rates among the different groups were compared using one-way ANOVA. The impact of varying levels of heart rate control on the risk of readmission for heart failure was assessed using the Cox proportional hazards model. Binary logistic regression analysis was employed to control for potential confounding factors. Results: We enrolled a total of 1136 patients with persistent atrial fibrillation. The results of the one-way ANOVA showed that there were differences in readmission rates among groups exposed to different levels of heart rate control. The readmission rates due to heart failure for each group were as follows: Group I (n=432): 31 (7.17%); Group II (n=387): 11.11%; Group III (n=317): 90 (28.50%) (F=54.3, P<0.001). After performing 1:1 propensity score matching for the different groups, 223 pairs were obtained. Analysis using the Cox proportional hazards model showed that compared to Group I, the risk of readmission for Group II was 1.372 (95% CI: 1.125-1.682, P<0.001), and for Group III was 2.053 (95% CI: 1.006-5.437, P<0.001). Furthermore, binary logistic regression analysis, including variables such as digoxin, hypertension, smoking, coronary heart disease, and chronic obstructive pulmonary disease as independent variables, revealed that coronary heart disease and COPD also had a significant impact on readmission due to heart failure (p<0.001). Conclusion: The correlation between the heart rate control level of patients with persistent atrial fibrillation and the risk of heart failure rehospitalization is positive. Reasonable heart rate control may significantly reduce the risk of heart failure rehospitalization.

Keywords: heart rate control levels, heart failure rehospitalization, persistent atrial fibrillation, retrospective cohort study

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