The emergence of Artificial Intelligence (AI) has brought many advancements in biomedical signal processing and analysis. It has opened the way for having efficient systems in the diagnosis and treatment of diseases such as Cardiovascular (CV) disorder. CV disorder is one of the critical health problems causing death to lots of peoples globally. Electrocardiogram (ECG) signal is the signal taken from the human body to diagnosis the status of CV and heart conditions. Earlier to the introduction of computers, the diagnosis of heart conditions was made by experts manually and that caused various mistakes. Currently, the usage of advancing signal processing devices help to reduce those errors and enables to develop effective signal detection and parameter estimation algorithms that are useful to analyze the parameters of ECG signals. Which intern supports to decide if the person is in critical condition and take an appropriate action. In this work, we analyze the performances of classical techniques and machine learning algorithms for ECG based CV parameters estimation. For this, first an in-depth review is done for both classical techniques and machine learning algorithms. Specifically, the benefits and challenges of machine learning and deep-learning algorithms for CV signal processing and parameter estimation is discussed. Then, we evaluate the performances of both classical (Kalman Filtering) and machine learning algorithms. The machine learning based algorithms are modeled with Butterworth low pass filter, wavelet transform and linear regression for parameter estimation. Besides, we propose an algorithm that combines adaptive Kalman filter (AKF) and discrete wavelet transform (DWT). In this algorithm, the ECG signal is filtered using AKF. Then, segmentation is performed and features are extracted by using DWT. Numerical simulation is done to validate the performances of these algorithms. The results show that at \({20}{\%}\) false positive rate, the detection performance of Kalman filtering, the proposed algorithm and machine learning algorithm are \({83}{\%}\), \({94}{\%}\) and \({97}{\%}\), respectively. That shows the proposed algorithm gives better performance than classical Kalman filtering and has nearly the same performance with machine learning algorithms.

Cardiovascular Signal Processing: State of the Art and Algorithms

About 15 million people alive today have been influenced by coronary illness. This is a major and critical issue in recent days. There are so many people have been lost their lives due to heart attack and other heart related issues. So, early on analysis and proper cure of heart disease is required to minimize the death rate due to heart disease. For better diagnosis we need exact and consistent tools for determine the fitness of human hearts to analysis the disease ahead of time before it makes around an undesirable changes in human body. For heart diagnosis one of the tools is Electrocardiogram (ECG) and the obtained signal is labeled ECG signal. This ECG signal contaminated by an amount of motion artifacts and noisy elements and deduction of these noisy elements from ECG signal must important before the ECG signal could be utilized for illness diagnosis purpose. There are various filter methods available for denoising ECG signal and select the best one on the dependence of performance parameter like signal to noise ratio (SNR) and power spectrum density (PSD).

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R. Jenkin Suji

Papers

Adaptive Kalman filter approach and Butterworth filter technique for ECG signal enhancement