Wavelet-Based Kernel Construction for Heart Disease Classification

Hải, Nguyễn Thanh; Nguyen, Nghia Thanh; Nguyễn, Mạnh Hùng; Livatino, Salvatore

doi:10.15598/aeee.v17i3.3270

Cited by 5 publications

(14 citation statements)

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“…These parameters allowed us to properly record a heartbeat without timer overflow and resulted in an ideal LCADC SNR of 73.25 dB. According to [4][5][6][12][13][14][15], an11-bit ADC resolution is appropriate and results in a precise, computer-based arrhythmia diagnosis. For the selected timer parameters, the obtained LCADC SNR wasequal to the theoretical SNR of an 11.9-bit classical ADC.…”

Section: F Timermentioning

confidence: 99%

“…For proper denoising, Frs i = Fre f C must bechosen [29]. The wavelet transform (WT) is frequently used for the multi-resolution time-frequency analysis of non-stationary ECG like signals [4,40]. This transform can be mathematically expressed by The wavelet transform (WT) is frequently used for the multi-resolution time-frequency analysis of non-stationary ECG like signals [4,40].…”

Section: Adaptive-rate Denoisingmentioning

confidence: 99%

“…Heart diseases are one of the major threats to human life [3], and a timely diagnosis can lead to better measures. To do this, individual heartbeats are analyzed by exploiting their frequency content and morphological patterns for the automatic diagnosis of ECG arrhythmia [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The denoised ECG signals are processed and analyzed to extract classifiable features that can assist in automatic diagnosis. Some broadly used features extraction techniques are wavelet packet decomposition (WPD) [4], wavelet-based kernel principle component analysis (wkPCA) [4], wavelet packet entropy (WPE) [5], discrete wavelet transform (DWT) [6,12,13], temporal and morphological features [12], principle component analysis (PCA) [13,14], bispectrum [14], and Hermite function coefficient and temporal features [15].…”

Section: Introductionmentioning

confidence: 99%

“…Supervised machine learning techniques are quite popular in these applications. In this regard, numerous approaches have been presented for ECG signal classification including those of the back propagation neural network (BNN) [4], random forest (RF) [5], probabilistic neural network (PNN) [6], support vector machine (SVM) [12], support vector machine (SVM)-radial basis function (RBF) [13,14], and optimized block-based neural network (OBNN) [15].…”

Section: Introductionmentioning

confidence: 99%

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Arrhythmia Diagnosis by Using Level-Crossing ECG Sampling and Sub-Bands Features Extraction for Mobile Healthcare

Qaisar

Hussain

2020

Sensors

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Mobile healthcare is an emerging technique for clinical applications. It is usually based on cloud-connected biomedical implants. In this context, a novel solution is presented for the detection of arrhythmia by using electrocardiogram (ECG) signals. The aim is to achieve an effective solution by using real-time compression, efficient signal processing, and data transmission. The system utilizes level-crossing-based ECG signal sampling, adaptive-rate denoising, and wavelet-based sub-band decomposition. Statistical features are extracted from the sub-bands and used for automated arrhythmia classification. The performance of the system was studied by using five classes of arrhythmia, obtained from the MIT-BIH dataset. Experimental results showed a three-fold decrease in the number of collected samples compared to conventional counterparts. This resulted in a significant reduction of the computational cost of the post denoising, features extraction, and classification. Moreover, a seven-fold reduction was achieved in the amount of data that needed to be transmitted to the cloud. This resulted in a notable reduction in the transmitter power consumption, bandwidth usage, and cloud application processing load. Finally, the performance of the system was also assessed in terms of the arrhythmia classification, achieving an accuracy of 97%.

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