ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors
| Autor: | Arnold C. Paglinawan, Edward B. Panganiban, Wen-Yaw Chung, Gilbert Lance S. Paa |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Pooling Wearable computer Convolutional neural network 02 engineering and technology 01 natural sciences Wearable biosensors Diagnostic support system Electrical and Electronic Engineering Artificial neural network business.industry Deep learning 010401 analytical chemistry Pattern recognition 021001 nanoscience & nanotechnology 0104 chemical sciences Electronic Optical and Magnetic Materials Electrocardiogram Identification (information) lcsh:TA1-2040 Signal Processing Spectrogram Noise (video) Artificial intelligence 0210 nano-technology business lcsh:Engineering (General). Civil engineering (General) Biotechnology |
| Zdroj: | Sensing and Bio-Sensing Research, Vol 31, Iss, Pp 100398-(2021) |
| ISSN: | 2214-1804 |
| Popis: | The latest developments in deep learning have made it possible to implement automated, advanced extraction of several things' features and classifications. Deep learning methods have also become more prominent in arrhythmia detection. This study conceptualized a classification method for ECG arrhythmia utilizing the Convolutional Neural Network (CNN) with images based on spectrograms without undergoing ECG visual examination such as R-peak or P-peak identification. This paper's CNN model would immediately disregard the noise parameter when its ECG data is converted into a 2D image while extracting the appropriate characteristic map in the pooling layer and convolution. Google's Inception V3 model was used to retrain the final layer of CNN for datasets recognition. This study established and formulated a diagnostic support system that enables the acquisition, interpretation, and analysis of clinical data and ECG biosignals from patients to facilitate heart disease diagnosis in rural areas or places where there is no ECG facility. Two ways were developed in training and testing the ECG datasets, the binary, and quinary classifications. These two classifications made a remarkable accuracy of 98.73% for binary and 97.33% for quinary. This study obtained a higher accuracy rate compared to the previous works. Specificity, sensitivity. Positive predictive values and F1 scores also made desirable results from 96.83% to 99.21%. Hence, we concluded that our system is an effective method in classifying heart rhythms to help the cardiologists in diagnostic analysis in the patient. |
| Databáze: | OpenAIRE |
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