Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods

Abstract

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

“…The HOG feature was first adopted for static pedestrian detection [36]; then, Rathikarani et al [37] used it to extract image features from ECGs, and classified three heart diseases: arrhythmia, myocardial infarction, and conduction block. SVM, RF, and K-NN, as classical machine learning algorithms, were also used in early studies of ECGs [38][39][40]. In the baseline method of this study, we employed the HOG algorithm to extract the image features from the 12-lead ECG and trained the aforementioned three classifiers for IGR diagnosis.…”

Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

“…The proposed architecture has offered lower energy consumption compared with the digital-CS scheme and enabled analog data compression. Tables 9-11 demonstrate the comparisons on power consumption, accuracy, SNR, CT, and Classification Accuracy (CA) based on K-Nearest Neighbor (K-NN) [47] for three types of sEMG bio-signals for the digital-CS and analog-CS scenarios. The highlights in Tables 9-11 can be classified as follows.…”

Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

“…The proposed architecture has offered lower energy consumption compared with the digital-CS scheme and enabled analog data compression. Tables 9 , 10 and 11 demonstrate the comparisons on power consumption, accuracy, SNR, CT, and Classification Accuracy (CA) based on K-Nearest Neighbor (K-NN) [ 47 ] for three types of sEMG bio-signals for the digital-CS and analog-CS scenarios. The highlights in Tables 9 , 10 and 11 can be classified as follows.…”