Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

Rizal, Achmad; Hadiyoso, Sugondo; Fauzi, Hilman; Widadi, Rahmat

doi:10.32985/ijeces.13.10.13

Cited by 3 publications

(2 citation statements)

References 22 publications

(31 reference statements)

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“…The statistical measures [26][27] such as Skewness, Kurtosis, Moment, Root Sum Square Value, Standard Deviation, Normalized First Difference, Second difference, variance, minimum and maximum entropy as well as the median can be considered for the feature extraction process. These features are utilized for IMF curves utilizing Hilbert-Huang and wavelet transforms to postulate the numerical value for all those curves, and it is utilized as the features.…”

Section: Statistical Featuresmentioning

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: Statistical Featuresmentioning

confidence: 99%

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

2024

IJIES

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“…Statistical computations on the case classification of electrocardiogram (ECG) signals are reported in [7]- [9]. Extraction of statistical features on cases of sleep apnea detection based on ECG signals reported in [10]. Statistical parameters are also used in ECG biometrics as reported in [11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of first order statistical processor on FPGA for feature extraction

Hadiyoso,

Ramdani,

Irawati

et al. 2024

IJRES

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Statistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%.

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Detection of Cardiac Arrhythmias in Patients with Obstructive Sleep Apnea using Machine Learning and Deep Learning Algorithms

Krithik,

Padmashree,

Nayak

et al. 2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

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Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

Cited by 3 publications

References 22 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

Implementation of first order statistical processor on FPGA for feature extraction

Detection of Cardiac Arrhythmias in Patients with Obstructive Sleep Apnea using Machine Learning and Deep Learning Algorithms

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