scholarly journals Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

2018 ◽  
Vol 17 (2) ◽  
pp. 7312-7325 ◽  
Author(s):  
Jyoti S Bali ◽  
Anilkumar V Nandi ◽  
P S Hiremath
Keyword(s):  
Sleep Apnea ◽  
Hilbert Transform ◽  
Signal Analysis ◽  
Principal Component ◽  
Feature Reduction ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Apnea Detection ◽  
Lm Algorithm

In this paper, a methodology for sleep apnea detection based on ECG signal analysis using Hilbert transform is proposed. The proposed work comprises a sequential procedure of preprocessing, QRS complex detection using Hilbert Transform, feature extraction from the detected QRS complex and the feature reduction using principal component analysis (PCA). Finally, the classification of the ECG signal recordings has been done using two different artificial neural networks (ANN), one trained with Levenberg-Marquardt (LM) algorithm and the other trained with Scaled Conjugate Gradient (SCG) method guided by K means clustering. The result of classification of the input ECG record is as either belonging to Apnea or Normal category. The performance measures of classification using the two classification algorithms are compared. The experimental results indicate that the SCG algorithm guided by K means clustering (ANN-SCG) has outperformed the LM algorithm (ANN-LM) by attaining accuracy, sensitivity and specificity values as 99.2%, 96% and 97% respectively, besides the saving achieved in terms of reduced number of principal components. Profiling time and mean square error of the ANN classifier trained with SCG algorithm is significantly reduced by 58% and 83%, respectively, as compared to LM algorithm.

scholarly journals ECG Signal Analysis on an Embedded Device for Sleep Apnea Detection

2020 ◽  
pp. 377-384
Author(s):  
Rishab Khincha ◽  
Soundarya Krishnan ◽  
Rizwan Parveen ◽  
Neena Goveas
Keyword(s):  
Sleep Apnea ◽  
Signal Analysis ◽  
Ecg Signal ◽  
Embedded Device ◽  
Apnea Detection

Detection of Sleep Apnea Disorder Using an Adaptive Neuro-Fuzzy Classification Method

2020 ◽  
Vol 17 (9) ◽  
pp. 4229-4234
Author(s):  
Jyoti Bali ◽  
Anilkumar V. Nandi ◽  
P. S. Hiremath ◽  
Poornima Patil
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Principal Component ◽  
Feature Reduction ◽  
Fuzzy Classification ◽  
Ecg Signal ◽  
Night Time ◽  
Fuzzy Classifier ◽  
Diagnostic Modality ◽  
Neuro Fuzzy

The proposed work aims at developing a solution for the detection of sleep apnea disorder using ECG signal analysis, which is an established diagnostic modality. Under this work, the standard research resource, ECG-Apnea database from MIT’s Physionet.org., having ECG signal night time recordings, is used. The sequential procedure of Preprocessing, Peak or QRS complex detection, Feature extraction, Feature reduction, and Classification is used. Preprocessing of the ECG signal is performed to free it from noise resulted from baseline wander, power-line interference, and muscle artifacts. Thus, the improved signal quality is estimated in terms of its Signal to Noise Ratio (SNR) and entropy value. QRS detection is implemented using the popular Pan-Tompkins algorithm that provides the reference for the feature extraction process. The performance of the detection algorithm is measured in terms of the average values of accuracy and specificity as 98% and 96%, respectively. Feature extraction algorithm involves the collection of selected 30 feature values related to the time domain and the frequency domain gathered from each of the test recordings of the ECG database, minute-wise for 7 hours. Feature reduction technique is followed to reduce the data size to a set of 20 ECG signal features using Principal Component Analysis (PCA) and avoid redundancy. Hence the trained Adaptive Neuro-Fuzzy Classifier is used on the output feature set derived from PCA to detect the presence or absence of Sleep apnea disorder with an estimated accuracy and specificity as 95% and 96%, respectively.

ECG Signal Analysis for Classification of Congenital Heart Defects

2020 ◽  
Author(s):  
Muhammad Umar Khan ◽  
Sumair Aziz ◽  
Mumtaz Ch. Javeria ◽  
Anber Shahjehan ◽  
Zohaib Mushtaq ◽  
...  
Keyword(s):  
Congenital Heart Defects ◽  
Signal Analysis ◽  
Congenital Heart ◽  
Heart Defects ◽  
Ecg Signal

Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

2019 ◽  
Vol 29 (02) ◽  
pp. 2050024
Author(s):  
Mahesh B. Dembrani ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Signal Analysis ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Digital Processor ◽  
Ica Algorithm

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

scholarly journals Swarm Intelligence Approach to QRS Detection

2020 ◽  
Vol 17 (4) ◽  
pp. 480-487
Author(s):  
Mohamed Belkadi ◽  
Abdelhamid Daamouche
Keyword(s):  
Signal Analysis ◽  
Optimization Problem ◽  
Noise Suppression ◽  
Parameter Design ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Algorithm Performance ◽  
Optimization Framework ◽  
Intelligence Approach

The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan-Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms

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