Artificial Intelligence-Based Speech Analysis System for Medical Support
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Purpose: Prior research has indicated that stroke can influence the symptoms and presentation of neurogenic bladder, with various patterns emerging, including abnormal facial and linguistic characteristics. Language patterns, in particular, can be easily recognized. In this paper, we propose a platform that accurately analyzes the voices of stroke patients with neurogenic bladder, enabling early detection and prevention of the condition. Methods: In this study, we developed an artificial intelligence-based speech analysis diagnostic system to assess the risk of stroke associated with neurogenic bladder disease in elderly individuals. The proposed method involves recording the voice of a stroke patient while they speak a specific sentence, analyzing it to extract unique feature data, and then offering a voice alarm service through a mobile application. The system processes and classifies abnormalities, and issues alarm events based on analyzed voice data. Results: In order to assess the performance of the software, we first obtained the validation accuracy and training accuracy from the training data. Subsequently, we applied the analysis model by inputting both abnormal and normal data and tested the outcomes. The analysis model was evaluated by processing 30 abnormal data points and 30 normal data points in real time. The results demonstrated a high test accuracy of 98.7% for normal data and 99.6% for abnormal data. Conclusions: Patients with neurogenic bladder due to stroke experience long-term consequences, such as physical and cognitive impairments, even when they receive prompt medical attention and treatment. As chronic diseases become increasingly prevalent in our aging society, it is essential to investigate digital treatments for conditions like stroke that lead to significant sequelae. This artificial intelligence-based healthcare convergence medical device aims to provide patients with timely and safe medical care through mobile services, ultimately reducing national social costs.
Abstract
Purpose: Prior research has indicated that stroke can influence the symptoms and presentation of neurogenic bladder, with various patterns emerging, including abnormal facial and linguistic characteristics. Language patterns, in particular, can be easily recognized. In this paper, we propose a platform that accurately analyzes the voices of stroke patients with neurogenic bladder, enabling early detection and prevention of the condition. Methods: In this study, we developed an artificial intelligence-based speech analysis diagnostic system to assess the risk of stroke associated with neurogenic bladder disease in elderly individuals. The proposed method involves recording the voice of a stroke patient while they speak a specific sentence, analyzing it to extract unique feature data, and then offering a voice alarm service through a mobile application. The system processes and classifies abnormalities, and issues alarm events based on analyzed voice data. Results: In order to assess the performance of the software, we first obtained the validation accuracy and training accuracy from the training data. Subsequently, we applied the analysis model by inputting both abnormal and normal data and tested the outcomes. The analysis model was evaluated by processing 30 abnormal data points and 30 normal data points in real time. The results demonstrated a high test accuracy of 98.7% for normal data and 99.6% for abnormal data. Conclusions: Patients with neurogenic bladder due to stroke experience long-term consequences, such as physical and cognitive impairments, even when they receive prompt medical attention and treatment. As chronic diseases become increasingly prevalent in our aging society, it is essential to investigate digital treatments for conditions like stroke that lead to significant sequelae. This artificial intelligence-based healthcare convergence medical device aims to provide patients with timely and safe medical care through mobile services, ultimately reducing national social costs.
참고문헌 (21)
- Özcan F, "The relationship between urinary symptom severity and functional status in patients with stroke", Scott Med J, 67, pp.64~70 (2022)
- Musco S, "Prevalence of nocturia after brain injury : a cross-sectional study in a single rehabilitation center", Brain Inj, 35, pp.90~95 (2021)
- Jun Yup Kim, "Executive Summary of Stroke Statistics in Korea 2018: A Report from the Epidemiology Research Council of the Korean Stroke Society", 대한뇌졸중영문학회지, 21(1), pp.42~59 (2019), 대한뇌졸중학회[DOI]
- Kim YS, "Stroke awareness decreases prehospital delay after acute ischemic stroke in Korea", BMC Neurol, 11, pp.2~ (2011)
- 홍근식, "Stroke Statistics in Korea: Part II Stroke Awareness and Acute Stroke Care, A Report from the Korean Stroke Society and Clinical Research Center For Stroke", 대한뇌졸중영문학회지, 15(2), pp.67~77 (2013), 대한뇌졸중학회[DOI]
- 박태환, "Identifying Target Risk Factors Using Population Attributable Risks of Ischemic Stroke by Age and Sex", 대한뇌졸중영문학회지, 17(3), pp.302~311 (2015), 대한뇌졸중학회
- Kim JW, "Prevalence and risk factors of elevated alanine aminotransferase among Korean adolescents : 2001-2014", BMC Public Health, 18, pp.617~ (2018)
- 채지운, "Trends and Risk Factors of Metabolic Syndrome among Korean Adolescents, 2007 to 2018", Diabetes and Metabolism Journal, 45(6), pp.880~889 (2021), 대한당뇨병학회[DOI]
- Keun-Sik Hong, "Stroke Statistics in Korea: Part I. Epidemiology and Risk Factors: A Report from the Korean Stroke Society and Clinical Research Center for Stroke", 대한뇌졸중영문학회지, 15(1), pp.2~20 (2013), 대한뇌졸중학회[DOI]
- Kamal N, "Delays in door-to-needle times and their impact on treatment time and outcomes in get with the guidelines-stroke", Stroke, 48, pp.946~954 (2017)
- Gaidhani BR, "Brain stroke detection using convolutional neural network and deep learning models", 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), pp.28~29 (2019)
- 김의선, "Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention", International Neurourology Journal, 26, pp.76~82 (2022), 대한배뇨장애요실금학회[DOI]
- Chin CL, "An automated early ischemic stroke detection system using CNN deep learning algorithm", 2017 IEEE 8th International conference on awareness science and technology (iCAST), pp.368~372 (2017)
- Elbagoury BM, "A hybrid stacked CNN and residual feedback GMDH-LSTM deep learning model for stroke prediction applied on mobile AI smart hospital platform", Sensors(Basel), 23, pp.3500~ (2023)
- Chavva IR, "Deep learning applications for acute stroke management", Ann Neurol, 92, pp.574~587 (2022)
- Fang G, "Predicting ischemic stroke outcome using deep learning approaches", Front Genet, 12, pp.827522~ (2022)
- Bijalwan V, "HDL-PSR : modelling spatio-temporal features using hybrid deep learning approach for post-stroke rehabilitation", Neural Process Lett, 55, pp.279~298 (2023)
- Wang H, "Deep learning for signal demodulation in physical layer wireless communications:prototype platform, open dataset, and analytics", IEEE Access, 7, pp.30792~30801 (2019)
- Wang Y, "Data-driven deep learning for automatic modulation recognition in cognitive radios", IEEE Trans Veh Technol, 68, pp.4074~4077 (2019)
- Chen W, "Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for headcut gully erosion susceptibility", J Environ Manage, 284, pp.112015~ (2021)
- Murali Subramaniyam, "Development of Mobile Application Program for Stroke Prediction Using Machine Learning with Voice Onset Time Data", Communications in Computer and Information Science, 1224, pp.670~675 (2020), Springer International Publishing[DOI]