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Published on: November 1, 2019
Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal
Sergey Filist1, Riad Taha Al-Kasasbeh2, Olga Shatalova1
1Department of Biomedical Engineering, Southwest State University, Kursk, Russia.
Insights
Early diagnosis of coronary heart disease (CHD) is crucial. This study developed a neural network model using electro cardio signals, improving diagnostic accuracy by 11% for earlier disease detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Coronary heart disease (CHD) is a leading global cause of mortality.
- Early diagnosis of cardiovascular diseases remains challenging, often relying on subjective physician experience.
- Current diagnostic methods like electrocardiography, sonography, and blood tests can be time-consuming and costly.
Purpose of the Study:
- To develop a novel classification model for assessing cardiovascular system function.
- To enable early detection of cardiovascular disease through advanced signal analysis.
- To improve the accuracy and efficiency of cardiovascular disease diagnosis.
Main Methods:
- Utilized electro cardio signals, analyzing the evolution of amplitudes of the first and second harmonics of the system rhythm (0.1 Hz).
- Separated signals into three streams: natural electro cardio signal and two derived from frequency analysis (amplitude- and frequency-detected).
- Employed a sliding window technique on demodulated electro cardio signals with amplitude and frequency detectors, feeding data into a neural network (NN) model.
Main Results:
- The developed NN model demonstrated a significant increase in diagnostic efficiency accuracy by 11%.
- The model effectively processes multiple signal streams derived from electro cardio signals.
- The system shows potential for objective and precise cardiovascular state assessment.
Conclusions:
- The developed neural network model offers a promising approach for the early and accurate detection of cardiovascular disease.
- This method enhances diagnostic capabilities beyond traditional assessments.
- The NN model can be trained for reliable early disease classification, potentially reducing diagnostic time and costs.
Abstract:
Coronary vascular disease (CHD) is one of the most fatal diseases worldwide. Cardio vascular diseases are not easily diagnosed in early disease stages. Early diagnosis is important for effective treatment, however, medical diagnoses are based on physician's personal experiences of the disease which increase time and testing cost to reach diagnosis. Physicians assess patients' condition based on electrocardiography, sonography and blood test results. In this research we develop classification model of the functional state of the cardiovascular system based on the monitoring of the evolution of the amplitudes of the first and second harmonics of the system rhythm of 0.1 Hz. We separate the signal to three streams; the first stream works with natural electro cardio signal, the other two streams are obtained as a result of frequency analysis of the amplitude- and frequency-detected electro cardio signal. We use sliding window of a demodulated electro cardio signal by means of amplitude and frequency detectors. The developed NN model showed an increase in accuracy of diagnostic efficiency by 11%. The neural network model can be trained to give accurate early detection of disease class.
