A New Heart Arrhythmia's Detection Algorithm

The paper proposes a simple algorithm for automatic detection of the R-peaks from a single lead digital ECG data. The squared double difference signal of the ECG data is used to localise the QRS regions. The proposed method consists of three stages: sorting and thresholding of the squared double difference signal of the ECG data to locate the approximate QRS regions, relative magnitude comparison in the QRS regions to detect the approximate R-peaks and RR interval processing to ensure accurate detection of peaks. The performance of the algorithm is tested on 12-lead ECG data from the PTB diagnostic ECG database, and a high detection sensitivity of 99.8% with low computational complexity and low sensitivity to low frequency noises is detected.

2017

An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. It is an important diagnostic tool for assessing heart functions. There are number of reasons which may affect its normal working. The objective of this thesis is to implement a simulation tool on MATLAB platform to detect abnormalities in the ECG signal and classification of heart disease by extracting features from ECG signals.

2016

The research article proposes the effective method for R-peak detection in the ECG signal. The improper beating of the heart called cardiac arrhythmia which is risk to human. The ECG samples are taken from physionet (physio bank ATM). Analysis of ECG signal and detection of R-Peaks is discussed in this paper. Initially the noise is removed from the signal using FFT technique, windowing technique and thresholding technique to detect R-peaks. In the ECG signal processing one can encounter the difficulties like unequal distance between peaks, irregular peak form, occurrence of lowfrequency components due to patient breathing etc., In order to resolve and reduce the effect of these factors processing pipeline should contain particular stages which is discussed in the paper and the R-peak detection algorithm is implemented in MATLAB R 2012b.

2020

In the work for processing the ECG signal, methods for determining the length of RR interval of ECG signal and calculating on its basis the boundaries of RR interval of ECG signal, geometric converting of RR intervals of ECG signal have been proposed. The proposed definition of the length of RR interval of ECG signal uses statistical estimation of local maximum and band-pass filtering, which decreases the computational complexity, and decreases the dependence on noise and permit to use dynamic threshold, which increases the accuracy of calculating the length and boundaries of RR intervals of ECG signal. The proposed geometric converting of RR intervals of ECG signal makes it possible to convert RR intervals to a unified amplitude-time window, which permits to form samples of ECG signal on basis its structure. The proposed model of ECG signal recognition is based on adaptive probabilistic neural network that allows identification of the structure and parameters, which increases the r...

Artificial Intelligence in Medicine, 2005

Arabian Journal for Science and Engineering, 2018

Electrocardiogram (ECG) signal processing and analysis is becoming more and more popular as it is useful in diagnosis and prognosis of human heart and clinically automatic machine estimation is based upon it. R-peak is the most important component in ECG beat and is widely used to investigate normal and abnormal subjects (patients). From the last few decades, R-peak detection in ECG has been the most challenging topic in the biomedical research. As QRS complex has high frequency in ECG as compared to other waves (P, T, U-wave), so majority of algorithms estimate QRS complex by either filtering or suppressing the lower frequency waves, including various artifacts like baseline wander, power line interference, and electromyograph noises. This paper demonstrates a new kind of ECG denoising algorithm based on self-convolution window (SCW) concept. The SCW based on Hamming window, herein referred to as Hamming self-convolution window, is used to design a new kind of filter which possesses negligible ripples in the stop band, as compared to the conventional window-based filters. This algorithm is validated on MIT-BIH arrhythmia database and the results outperform in terms of sensitivity, positive predictivity, and error rate obtained as 99.93%, 99.95%, and 0.117%, respectively, as compared to the other well-established works. The approach has also outperformed the results of well-established window-based filters (Hamming and Kaiser) in terms of reduced false negative, false positive, and error rate.

The International Journal of Innovative Research in Science, Engineering and Technology, 2018

Electrocardiogram (ECG)is a signal that records the electrical performance of the heart. A reliable, real time analysis of ECG and its considerate features is a necessary prerequisite for monitoring R-R Interval, hence HRV and cardiovascular control. The work proposed in this paper reviews the various methods used to detect the R-R interval and its related features that summarizes the various techniques used by researchers and also provides generic advantages about the various methods used. The processing of the data was done on the Lead-II ECG signals and on a tool called as Matlab.

International Journal of Electrical and Computer Engineering , 2019

It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to detect QRS complex. This method uses a new approach for adapting threshold level, which is based on statistical analysis of the signal. Forty-eight records from an existing arrhythmia database have been tested using the modified method. The result of the proposed method shows the high performance metrics with sensitivity of 99.62% and a positive predictivity of 99.88% for QRS complex detection. 1. INTRODUCTION Heart disease and cardiac stroke are the most leading causing of fatalities around the world in the last 15 years. These diseases were responsible for a 15.2 million deaths in 2016 [1]. The necessity and urgency of dealing and early detecting of these diseases were the motivation behind many publications and research center tasks. Different types of physiological signals can be captured from a human body to detect some signs of heart disease. The most detectable signal is the Electrocardiogram (ECG) which representative of the cyclical rhythm of human heart muscles. Heart muscle rhythm is driven by electrical pulses. ECG instruments can sense such electrical pulses because of its strength by electrodes positioned on the human skin [2, 3]. These electrical pulses, represented ECG, can be plotted or saved in a format that can be interpreted by the specialists. ECG shape provides much information about heart state such as time interval and amplitude. Many features and metrics, consisting of many characteristic points, can detect cardiac abnormalities or behavioral changes such as heart rate variability [4]. Different segments of ECG signal have been used to detect the heart abnormalities. The QRS complex is considered one of the most significant parts of ECG signals. Pan and Tompkins [5] developed a method for the QRS complex detection. This method had used the assembly language and implementation was on a Z80 microprocessor. The performance of their method was deeply affected by frequency variation in QRS complexes which represented a main drawback of this algorithm. Therefore, a more adaptive real time QRS detection algorithm had been suggested by the same authors and implemented using the C language [6].

International Journal of Computer Applications, 2015

A condition in which the heart beats with an irregular or abnormal rhythm is known as Arrhythmia. This paper presents a procedure to extract information from Electrocardiogram (ECG) data & determine types of Arrhythmias. The decisions were achieved by determining different intervals such as PR Interval, RR Interval, Heart Rate (HR) etc. and those intervals were compared with the ideal intervals. During the whole process MATLAB was used & ECG signals were taken from PhysioBank ATM. In this process Savitzky-Golay filter was used to reduce the noise of the signal. Tachycardia, Bradycardia, Heart Block, Junctional Arrhythmia, Premature Articular Contraction were detected during this analysis. The results show simplified detection of arrhythmia with 90%accuracy.

This paper presents an algorithm for Electrocardiogram (ECG) analysis to detect and classify ECG waveform anomalies and abnormalities. This is achieved by extracting various features and durations of the ECG waveform such as RR interval, QRS complex, P wave and PR durations. These durations are then compared with normal values to determine the degree and types of abnormalities. Most of the data used for this study were extracted from the MIT-BIH arrhythmia database while some data was extracted from ECG recordings acquired specifically for the purposes of this study. The paper is concluded with detailed results obtained from testing the algorithm using the ECG data.