Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Li, Hongqiang; An, Zhixuan; Zuo, Shasha; Zhu, Wei; Cao, Lu; Mu, Yuxin; Song, Wen‐Chao; Mao, Quanhua; Zhang, Zhen; Li, Enbang; Prades, Joan Daniel

doi:10.1007/s11517-021-02461-4

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…However, the Artificial Neural Network (ANN) is capable of learning the features for classification by training [10,11]. The statistical features named variance, kurtosis, skewness and so on are estimated from the ECG segmentation to categorize abnormal heartbeats from normal utilizing Machine learning (ML)-based classification algorithms [12,13]. To enhance the performance and dimensionality reduction, various Deep learning (DL) approaches have been developed [14,15].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

2024

IJIES

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The Electrocardiogram (ECG) serves as a crucial indicator of diverse cardiac conditions, emphasizing the importance of precise signal classification for automated arrhythmia detection. ECG is an efficient tool for the diagnosis and detection of arrhythmia. Detecting arrhythmias in extended ECG segments can result in episodes being overlooked. However, since ECG transmits a massive amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. To overcome this problem, this research proposes Long Short-Term Memory (LSTM) with Luong Attention Mechanism approach for the classification of arrhythmia from ECG into 5 classes. When LSTM is combined with Luong attention, they can learn which parts of the ECG signal are crucial at each time step, and effectively capturing both short-term and long-term dependencies. For evaluating the performance of the proposed method, the data is collected from a benchmark dataset called the MIT-BIH dataset. After the collection of the dataset, the pre-processing is done using Continuous Wavelet Transform (CWT) to reduce the low and high-frequency noise. After that, the pre-processed data is forwarded to the feature extraction process to extract the relevant features by using the statistical (Skewness, Kurtosis, Moment, etc.) and time-frequency domain features. Finally, the LSTM is used to classify the arrhythmia classes. From this analysis, the proposed LSTM with Luong Attention Mechanism achieved better results in overall metrics. The proposed method achieves the accuracy of 99.75% which is comparatively higher than the existing approaches like Deep Residual Convolutional Neural Network (DRCNN) and Depth wise Separable CNN with Focal Loss (DSC-FL-CNN).

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Section: Introductionmentioning

confidence: 99%

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

2024

IJIES

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“…In order to do that, we will try K-fold validation and other comparable approaches in the future together with a federated learning strategy. The cardiac ailment known as arrhythmia (ARR) affects the pace at which the heart beats [4]. Inappropriate electrical impulse production, which controls the heartbeat, is one of the primary causes.…”

Section: Introductionmentioning

confidence: 99%

Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation

Jawad,

Merza,

Sim

2024

IJ-AI

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<span lang="EN-US">Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.</span>

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“…They include bioelectric, mechano‐electric, electrochemical, optoelectronic, and ultrasonic sensors. [ 5 ] For example, heart rate can be obtained by optoelectric, mechano‐electric, or ultrasonic type sensors utilizing the pulsatile signal waveforms obtained from ECG, [ 6 ] photoplethysmography (PPG), [ 7 ] seismocardiography [ 8 ] ballistography [ 9 ] and ultrasonography. [ 10 ] SpO 2 can be obtained by monitoring the PPG signals from the subcutaneous arteries using optoelectronic sensors.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO₂ Monitoring

Dcosta,

Ochoa,

Sanaur

2023

Advanced Science

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Flexible and wearable biosensors are the next‐generation healthcare devices that can efficiently monitor human health conditions in day‐to‐day life. Moreover, the rapid growth and technological advancements in wearable optoelectronics have promoted the development of flexible organic photoplethysmography (PPG) biosensor systems that can be implanted directly onto the human body without any additional interface for efficient bio‐signal monitoring. As an example, the pulse oximeter utilizes PPG signals to monitor the oxygen saturation (SpO2) in the blood volume using two distinct wavelengths with organic light emitting diode (OLED) as light source and an organic photodiode (OPD) as light sensor. Utilizing the flexible and soft properties of organic semiconductors, pulse oximeter can be both flexible and conformal when fabricated on thin polymeric substrates. It can also provide highly efficient human‐machine interface systems that can allow for long‐time biological integration and flawless measurement of signal data. In this work, a clear and systematic overview of the latest progress and updates in flexible and wearable all‐organic pulse oximetry sensors for SpO2 monitoring, including design and geometry, processing techniques and materials, encapsulation of various factors affecting the device performance, and limitations are provided. Finally, some of the research challenges and future opportunities in the field are mentioned.

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Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Cited by 13 publications

References 21 publications

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO₂ Monitoring

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Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Cited by 13 publications

References 21 publications

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

Electrocardiogram Based Arrhythmia Classification Using Long Short-Term Memory with Luong Attention Mechanism

Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO2 Monitoring

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Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO₂ Monitoring