Search results for: minimally invasive lumbar discectomy

Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

Abstract:

Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

Procedia PDF Downloads 46

Authors: Pei Yi Lin

Abstract:

Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

Procedia PDF Downloads 39

Authors: Kyriaki Hatziagapiou, Eleni Kakouri, Konstantinos Bethanis, Alexandra Nikola, Eleni Koniari, Charalabos Kanakis, Elias Christoforides, George Lambrou, Petros Tarantilis

Abstract:

Medulloblastoma is a highly invasive tumour, as it tends to disseminate throughout the central nervous system early in its course. Despite the high 5-year-survival rate, a significant number of patients demonstrate serious long- or short-term sequelae (e.g., myelosuppression, endocrine dysfunction, cardiotoxicity, neurological deficits and cognitive impairment) and higher mortality rates, unrelated to the initial malignancy itself but rather to the aggressive treatment. A strong rationale exists for the use of Crocus sativus L (saffron) and its bioactive constituents (crocin, crocetin, safranal) as pharmaceutical agents, as they exert significant health-promoting properties. Crocins are water soluble carotenoids. Unlike other carotenoids, crocins are highly water-soluble compounds, with relatively low toxicity as they are not stored in adipose and liver tissues. Crocins have attracted wide attention as promising anti-cancer agents, due to their antioxidant, anti-inflammatory, and immunomodulatory effects, interference with transduction pathways implicated in tumorigenesis, angiogenesis, and metastasis (disruption of mitotic spindle assembly, inhibition of DNA topoisomerases, cell-cycle arrest, apoptosis or cell differentiation) and sensitization of cancer cells to radiotherapy and chemotherapy. The current research aimed to study the potential cytotoxic effect of crocins on TE671 medulloblastoma cell line, which may be useful in the optimization of existing and development of new therapeutic strategies. Crocins were extracted from stigmas of saffron in ultrasonic bath, using petroleum-ether, diethylether and methanol 70%v/v as solvents and the final extract was lyophilized. Identification of crocins according to high-performance liquid chromatography (HPLC) analysis was determined comparing the UV-vis spectra and the retention time (tR) of the peaks with literature data. For the biological assays crocin was diluted to nuclease and protease free water. TE671 cells were incubated with a range of concentrations of crocins (16, 8, 4, 2, 1, 0.5 and 0.25 mg/ml) for 24, 48, 72 and 96 hours. Analysis of cell viability after incubation with crocins was performed with Alamar Blue viability assay. The active ingredient of Alamar Blue, resazurin, is a blue, nontoxic, cell permeable compound virtually nonfluorescent. Upon entering cells, resazurin is reduced to a pink and fluorescent molecule, resorufin. Viable cells continuously convert resazurin to resorufin, generating a quantitative measure of viability. The colour of resorufin was quantified by measuring the absorbance of the solution at 600 nm with a spectrophotometer. HPLC analysis indicated that the most abundant crocins in our extract were trans-crocin-4 and trans-crocin-3. Crocins exerted significant cytotoxicity in a dose and time-dependent manner (p < 0.005 for exposed cells to any concentration at 48, 72 and 96 hours versus cells not exposed); as their concentration and time of exposure increased, the reduction of resazurin to resofurin decreased, indicating reduction in cell viability. IC50 values for each time point were calculated ~3.738, 1.725, 0.878 and 0.7566 mg/ml at 24, 48, 72 and 96 hours, respectively. The results of our study could afford the basis of research regarding the use of natural carotenoids as anticancer agents and the shift to targeted therapy with higher efficacy and limited toxicity. Acknowledgements: The research was funded by Fellowships of Excellence for Postgraduate Studies IKY-Siemens Programme.

Keywords: crocetin, crocin, medulloblastoma, saffron

Procedia PDF Downloads 186