M. Sabarimalai Manikandan

A novel method for detecting R-peaks in electrocardiogram (ECG) signal

Abstract The R-peak detection is crucial in all kinds of electrocardiogram (ECG) applications. However, almost all existing R-peak detectors suffer from the non-stationarity of both QRS morphology and noise. To combat this difficulty, we propose a new R-peak detector, which is based on the new preprocessing technique and an automated peak-finding logic. In this paper, we first demonstrate that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks in case of wider and small QRS complexes, negative QRS polarities, and sudden changes in QRS amplitudes over that using the absolute value, energy value, and Shannon entropy features. Then we justify the simplicity and robustness of the proposed peak-finding logic using the Hilbert-transform (HT) and moving average (MA) filter. The proposed R-peak detector is validated using the first-channel of the 48 ECG records of the MIT-BITH arrhythmia database, and achieves average detection accuracy of 99.80%, sensitivity of 99.93% and positive predictivity of 99.86%. Various experimental results show that the proposed R-peak detection method significantly outperforms other well-known methods in case of noisy or pathological signals.

Wavelet energy based diagnostic distortion measure for ECG

Abstract In this paper, a novel Wavelet Energy based diagnostic distortion (WEDD) measure is proposed to assess the reconstructed signal quality for ECG compression algorithms. WEDD is evaluated from the Wavelet coefficients of the original and the reconstructed ECG signals. For each ECG segment, a Wavelet energy weight vector is computed via five-level biorthogonal discrete Wavelet transform (DWT). WEDD is defined as the sum of Wavelet energy weighted percentage root mean square difference of each subband. The effectiveness of this measure is validated by linear (linear polynomial and cubic polynomial) and nonlinear (logistic) regression analysis between the computed WEDD values and the mean opinion score (MOS) given by cardiologists. WEDD provides a better prediction accuracy and exhibits a statistically better monotonic relationship with the MOS ratings than Wavelet based weighted percentage root mean square difference (PRD) measure (WWPRD), PRD and other objective measures. Standard correlation coefficient and Spearman rank-order correlation coefficient (SROCC) between the WEDD/MOS ratings is 0.969 and 0.9624, respectively.

Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM

Abstract In this paper, a new Wavelet threshold based ECG signal compression technique using uniform scalar zero zone quantizer (USZZQ) and Huffman coding on differencing significance map (DSM) is proposed. Wavelet coefficients are selected based on the energy packing efficiency of each sub-band. Significant Wavelet coefficients are quantized with uniform scalar zero zone quantizer. Significance map is created to store the indices of the significant coefficients. This map is encoded efficiently with less number of bits by applying Huffman coding on the differences between indices in the significance map. ECG records from the MIT-BIH arrhythmia database are selected as test data. For the record 117, the proposed technique achieves a compression ratio of 18.7:1 with lower percentage root mean square difference (PRD) compared to other threshold based methods. The proposed technique is tested for MIT-BIH arrhythmia record 119 and a compression ratio of 21.81:1 is achieved with a PRD value of 3.716% which is much lower compared to the reported PRD value of 5.0 and 5.5% of set partitioning in hierarchical tress (SPIHT) and analysis by synthesis ECG compressor (ASEC), respectively. The noise eliminating capability of the proposed technique is also demonstrated in this work. The proposed technique achieves the required compression ratio with less reconstruction error for GSM-based cellular telemedicine system.

Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries

Several methods have been proposed for detection and classification of power quality (PQ) disturbances using wavelet, Hilbert transform, Gabor transform, Gabor-Wigner transform, S transform, and Hilbert-Haung transform. This paper presents a new method for detection and classification of single and combined PQ disturbances using a sparse signal decomposition (SSD) on overcomplete hybrid dictionary (OHD) matrix. The method first decomposes a PQ signal into detail and approximation signals using the proposed SSD technique with an OHD matrix containing impulse and sinusoidal elementary waveforms. The output detail signal adequately captures morphological features of transients (impulsive and oscillatory) and waveform distortions (harmonics and notching). Whereas the approximation signal contains PQ features of fundamental, flicker, dc-offset, and short- and long-duration variations (sags, swells, and interruptions). Thus, the required PQ features are extracted from the detail and approximation signals. Then, a hierarchical decision-tree algorithm is used for classification of single and combined PQ disturbances. The proposed method is tested using both synthetic and microgrid simulated PQ disturbances. Results demonstrate the accuracy and robustness of the method in detection and classification of single and combined PQ disturbances under noiseless and noisy conditions. The method can be easily expanded for compressed sensing based PQ monitoring networks.

Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review

Abstract Cardiovascular disease (CVD) is one of the most widespread health problems with unpredictable and life-threatening consequences. The electrocardiogram (ECG) is commonly recorded for computer-aided CVD diagnosis, human emotion recognition and person authentication systems. For effective detection and diagnosis of cardiac diseases, the ECG signals are continuously recorded, processed, stored, and transmitted via wire/wireless communication networks. But long-term continuous cardiac monitoring system generates huge volume of ECG data daily. Therefore, a reliable and efficient ECG signal compression method is highly demanded to meet the real-time constraints including limited channel capacity, memory and battery-power of remote cardiac monitoring, ECG record management and telecardiology systems. In such scenarios, the main objective of the ECG signal compression is to reduce the data rate for effective transmission and/or storage purposes without significantly distorting the clinical features of different kinds of PQRST morphologies contained in the recorded ECG signal. Numerous ECG compression methods have been proposed by exploiting the intra-beat correlation, inter-beat correlation and intra-channel correlation of the ECG signals. This paper presents a prospective review of wavelet-based ECG compression methods and their performances based upon findings obtained from various experiments conducted using both clean and noisy ECG signals. This paper briefly describes different kinds of compression techniques used in the one-dimensional wavelet-based ECG compression methods. Then, the performance of each of the wavelet-based compression methods is tested and validated using the standard MIT-BIH arrhythmia databases and performance metrics. The pros and cons of different wavelet-based compression methods are demonstrated based upon the experimental results. Finally, various practical issues involved in the validation procedures, reconstructed signal quality assessment, and performance comparisons are highlighted by considering the future research studies based on the recent powerful digital signal processing techniques and computing platform.

ECG Signal Compression using Discrete Sinc Interpolation

This paper presents a novel ECG data compression algorithm based on discrete sinc interpolation (DSI) technique. The compression and decompression of ECG data is achieved using discrete sinc interpolation (DSI), which is realized by an efficient discrete Fourier transform (DFT). The proposed algorithm is evaluated using MIT-BIH arrhythmia database (sampled at 360 Hz with 11 bits resolution). The performance of the proposed DSI based algorithm is compared with the performance of the widely used ECG data compression algorithms such as AZTEC, FAN, Hilton and Djohan algorithms. It is observed that higher compression ratio (CR) is achieved with a relatively lower percentage RMS difference (PRD) by DSI algorithm. The diagnostic distortion is measured in terms of average absolute error (AAE), which is lower in case of the DSI algorithm compared to the AZTEC and FAN algorithm

Straightforward and robust QRS detection algorithm for wearable cardiac monitor

This Letter presents a fairly straightforward and robust QRS detector for wearable cardiac monitoring applications. The first stage of the QRS detector contains a powerful ℓ1-sparsity filter with overcomplete hybrid dictionaries for emphasising the QRS complexes and suppressing the baseline drifts, powerline interference and large P/T waves. The second stage is a simple peak-finding logic based on the Gaussian derivative filter for automatically finding locations of R-peaks in the ECG signal. Experiments on the standard MIT-BIH arrythmia database show that the method achieves an average sensitivity of 99.91% and positive predictivity of 99.92%. Unlike existing methods, the proposed method improves detection performance under small-QRS, wide-QRS complexes and noisy conditions without using the searchback algorithms.

Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring

In this paper, we propose a novel signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications. The proposed quality-aware ECG monitoring system consists of three modules: 1) ECG signal sensing module; 2) automated signal quality assessment (SQA) module; and 3) signal-quality aware (SQAw) ECG analysis and transmission module. The main objectives of this paper are: design and development of a light-weight ECG SQA method for automatically classifying the acquired ECG signal into acceptable or unacceptable class and real-time implementation of proposed IoT-enabled ECG monitoring framework using ECG sensors, Arduino, Android phone, Bluetooth, and cloud server. The proposed framework is tested and validated using the ECG signals taken from the MIT-BIH arrhythmia and Physionet challenge databases and the real-time recorded ECG signals under different physical activities. Experimental results show that the proposed SQA method achieves promising results in identifying the unacceptable quality of ECG signals and outperforms existing methods based on the morphological and RR interval features and machine learning approaches. This paper further shows that the transmission of acceptable quality of ECG signals can significantly improve the battery lifetime of IoT-enabled devices. The proposed quality-aware IoT paradigm has great potential for assessing clinical acceptability of ECG signals in improvement of accuracy and reliability of unsupervised diagnosis system.

An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery

In this paper, we present an effective pre-processing algorithm for band selection approach which is an essential task in hyperspectral image analysis. The pre-processing algorithm is developed based on the average inter-band block-wise correlation coefficient measure and a simple thresholding strategy. Here, the threshold parameter is found based on the standard deviation of the average inter-band block-wise correlation coefficients. The performance of the proposed algorithm is validated using the standard hyperspectral database created by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the detected bands with ground-truth annotations, we observed that the proposed algorithm identifies the noisy and water absorption bands in the high-dimensional hyperspectral images. The proposed algorithm achieves the classification accuracy of 94.73%.

A simple and robust QRS detection algorithm for wireless medical body area network

Recently, wireless medical body area network (WMBAN) plays an important role in remote cardiac patient monitoring, intelligent emergency care management system, and ubiquitous mobile healthcare applications. The wearable cardiac monitoring devices used in WMBAN system collect and transmit the vital signs of cardiac patients continuously. Generally, the use of WMBAN technology is restricted by size, power consumption, transmission capacity (bandwidth), and computational loads. Therefore, there is a great demand for low-complexity cardiac signal processing algorithms that can combat some of technical challenges related to pervasive healthcare computing with WMBAN technologies. In this paper, we present low complexity automatic QRS detection algorithm for long-term wearable cardiac monitoring device. The proposed QRS detection method first derives a smooth Shannon energy envelogram (SEE) of the first-derivative of the filtered ECG at the preprocessing stage. The major local maxima (LM) in the smooth SEE indicate the approximate locations of the R-peaks. In the second stage, the proposed HT-based peak-finding logic identifies the locations of the LM by detecting the positive zero-crossings in the HT of the SEE. Finally, the locations of the LM are used as guides to find the accurate locations of the R-peaks in the ECG signal. The proposed method is validated using the standard MIT-BIH arrhythmia database, and achieves an overall sensitivity of 99.86% and positive predictivity of 99.95%. Various experimental results show that the proposed algorithm significantly outperforms other well-known algorithms in case of noisy or pathological signals.