Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Abstract

Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device. AbstrakDeteksi Otomatis Detak Arrhythmia: Algoritma, Sistem dan Implementasi. Penyakit jantung adalah salah satu penyebab utama kematian di dunia. Diagnosa awal dari penyakit tersebut didasarkan pada ketidaknormalan pola detak jantung, atau disebut juga sebagai Arrhythmia. Sebuah metode baru bernama fuzzy neuro generalized learning vector quantization (FN-GLCQ) kami kembangkan untuk melakukan klasifikasi terhadap pola tersebut. Algoritma tersebut dimodifikasi dari algoritma GLVQ dengan mengimplementasikan konsep fuzzy logic sebagai fungsi diskriminan untuk meningkatkan performa klasifikasi. Algoritma tersebut dites dengan menggunakan MIT-BIH arrythmia database untuk dianalisis performanya. Berdasarkan uji coba yang telah dilakukan, FN-GLVQ dapat meningkatkan akurasi dari performa GLVQ. Sesuai dengan tujuan kami untuk membangun alat pendeteksian otomatis, FN-GLVQ juga diimplementasikan pada Field Gate Programmable Array sebagai prototipe dari perangkat yang akan dibangun.

Developing smart Tele-ECG system for early detection and monitoring heart diseases based on ECG signal: progress and challenges

“…However, in order to extract the features of the beats, we will need to extract the continuous beats into individual beats. This process will utilize the cutoff technique, as previously conducted in the study of Jatmiko et al (2016). The method approximates each individual beat to be 300 data points in length.…”

“…The technique will determine the outlier by creating a boundary from the percentile of the data. In the study of Jatmiko et al (2016), they chose the upper quartile (Q 1 ) in the 25th percentile and chose the lower quartile (Q 2 ) in the 75th percentile. The interquartile range (IQR) can be calculated by using the following equation:…”

“…Therefore, it is essential to extract the best features from the data set, as selecting better features will lead to better outcome during the classification process. To extract the features, wavelet transformation is proven effective, as shown in the studies of Setiawan et al (2011) andJatmiko et al (2016). The wavelet transform definition can be seen in the following equation:…”

“…In the case of the arrhythmia's usage of the ECG signals, Daubechies is selected as the mother wavelet since it achieves a better performance as, described by Senhadji et al (1995). In the studies of Setiawan et al (2011) and Jatmiko et al (2016), Daubechies level 8 proved to have a better result than the other mother wavelet. The research then used the db8 as the mother wavelet and decomposed the individual beats from 1 to 5.…”

“…The coefficient that will represent the signal has to be selected properly in order to select the best features that represent the signal. In the studies of Setiawan et al (2011) and Jatmiko et al, (2016), d2, d3, d4, and d5 represent the high-frequency signal. a1, a2, a3, a4, and a5 represent the signal approximation; hence, it has the important features of the signal.…”