Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to redu...Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to reduce night-time events. We clarified the characteristics of the dynamic nature of heartbeats associated with periodic breathing by using detrended fluctuation analysis (DFA). We analyzed heartbeats in eight recordings from the MIT-BIH Polysomnographic database. We observed two crossover points and defined three scaling exponents, β1 (n ≤ 40 beats), β2 (50 ≤ n ≤ 200), and β3 (251 ≤ n ≤ 1584). Compared with β1 (1.21 ± 0.13) and β3 (0.92 ± 0.16), scaling exponent β2 (0.62 ± 0.16) showed the statistically lowest value (p 0.05). And there was a negative relationship between the scaling exponent β2 and apnea/hypopnea index (p 0.05). These results indicate that DFA analysis of heartbeats may be useful for the early detection of sleep associated breathing disorders including sleep apnea and its severity.展开更多
Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardi...Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardiogram(ECG)heartbeats,comparing traditional feature-based ML methods with innovative image-based approaches.The dataset underwent rigorous preprocessing,including down-sampling,frequency filtering,beat segmentation,and normalization.Two methodologies were explored:(1)handcrafted feature extraction,utilizing metrics like heart rate variability and RR distances with LightGBM classifiers,and(2)image transformation of ECG signals using Gramian Angular Field(GAF),Markov Transition Field(MTF),and Recurrence Plot(RP),enabling multimodal input for convolutional neural networks(CNNs).The Synthetic Minority Oversampling Technique(SMOTE)addressed data imbalance,significantly improving minority-class metrics.Results:The handcrafted feature approach achieved notable performance,with LightGBM excelling in precision and recall.Image-based classification further enhanced outcomes,with a custom Inception-based CNN,attaining an 85%F1 score and 97%accuracy using combined GAF,MTF,and RP transformations.Statistical analyses confirmed the significance of these improvements.Conclusion:This work highlights the potential of ML for cardiac irregularities detection,demonstrating that combining advanced preprocessing,feature engineering,and state-of-the-art neural networks can improve classification accuracy.These findings contribute to advancing AI-driven diagnostic tools,offering promising implications for cardiovascular healthcare.展开更多
1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of t...1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of the heart in time,allowing accurate diagnoses by clinicians[1].Despite the relative simplicity of ECG acquisition,its interpretation requires extensive training.Manual examination and re-examination of ECG paper records can be time-consuming,potentially delaying diagnosis.Machine learning,which uses algorithms to identify patterns within data and make predictive analyses,has played a significant role in interpreting ECGs[2].展开更多
BACKGROUND Heart rate variability(HRV)was shown to be affected by performing religious activities.AIM To examine the relationship between the level of Quran(the holy book of Muslims)memorisation and HRV among teenager...BACKGROUND Heart rate variability(HRV)was shown to be affected by performing religious activities.AIM To examine the relationship between the level of Quran(the holy book of Muslims)memorisation and HRV among teenagers.METHODS This experimental study included 16 Tahfiz students and 16 non-Tahfiz students(n=32).The HRV was measured in three tasks:Recalling familiar verses,memorising new verses,and recalling the newly memorised verses of the Quran.HRV analysis was done using these parameters:Standard deviation of N-N(heartbeat peak)interval;low frequency(LF);high frequency(HF)and LF/HF ratio.RESULTS There were significant differences between tasks for all parameters(P<0.05).However,between the groups,only the LF/HF ratio had significant differences,with F=5.04,P<0.05.Pearson correlation showed a moderate positive correlation between the number of pages memorised and the LF/HF ratio(r=0.61,P<0.05).CONCLUSION Quran memorisation increased the HRV and our results suggested that this activity could be developed as an effective sympathovagal modulation training activity.展开更多
The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract ...The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.展开更多
Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences betwee...Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.展开更多
文摘Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to reduce night-time events. We clarified the characteristics of the dynamic nature of heartbeats associated with periodic breathing by using detrended fluctuation analysis (DFA). We analyzed heartbeats in eight recordings from the MIT-BIH Polysomnographic database. We observed two crossover points and defined three scaling exponents, β1 (n ≤ 40 beats), β2 (50 ≤ n ≤ 200), and β3 (251 ≤ n ≤ 1584). Compared with β1 (1.21 ± 0.13) and β3 (0.92 ± 0.16), scaling exponent β2 (0.62 ± 0.16) showed the statistically lowest value (p 0.05). And there was a negative relationship between the scaling exponent β2 and apnea/hypopnea index (p 0.05). These results indicate that DFA analysis of heartbeats may be useful for the early detection of sleep associated breathing disorders including sleep apnea and its severity.
文摘Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardiogram(ECG)heartbeats,comparing traditional feature-based ML methods with innovative image-based approaches.The dataset underwent rigorous preprocessing,including down-sampling,frequency filtering,beat segmentation,and normalization.Two methodologies were explored:(1)handcrafted feature extraction,utilizing metrics like heart rate variability and RR distances with LightGBM classifiers,and(2)image transformation of ECG signals using Gramian Angular Field(GAF),Markov Transition Field(MTF),and Recurrence Plot(RP),enabling multimodal input for convolutional neural networks(CNNs).The Synthetic Minority Oversampling Technique(SMOTE)addressed data imbalance,significantly improving minority-class metrics.Results:The handcrafted feature approach achieved notable performance,with LightGBM excelling in precision and recall.Image-based classification further enhanced outcomes,with a custom Inception-based CNN,attaining an 85%F1 score and 97%accuracy using combined GAF,MTF,and RP transformations.Statistical analyses confirmed the significance of these improvements.Conclusion:This work highlights the potential of ML for cardiac irregularities detection,demonstrating that combining advanced preprocessing,feature engineering,and state-of-the-art neural networks can improve classification accuracy.These findings contribute to advancing AI-driven diagnostic tools,offering promising implications for cardiovascular healthcare.
基金supported by the NSFC-FDCT Grant 62361166662the National Key R&D Program of China(2023YFC3503400,2022YFC3400400)+4 种基金the Innovative Research Group Project of Hunan Province(2024JJ1002)the Key R&D Program of Hunan Province(2023GK2004,2023SK2059,2023SK2060)the Top 10 Technical Key Project in Hunan Province(2023GK1010)the Key Technologies R&D Program of Guangdong Province(2023B1111030004 to FFH)the Funds of the National Supercomputing Center in Changsha.
文摘1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of the heart in time,allowing accurate diagnoses by clinicians[1].Despite the relative simplicity of ECG acquisition,its interpretation requires extensive training.Manual examination and re-examination of ECG paper records can be time-consuming,potentially delaying diagnosis.Machine learning,which uses algorithms to identify patterns within data and make predictive analyses,has played a significant role in interpreting ECGs[2].
基金Supported by the Research Grant from Universiti Kebangsaan Malaysia,No.GGP-2017-061.
文摘BACKGROUND Heart rate variability(HRV)was shown to be affected by performing religious activities.AIM To examine the relationship between the level of Quran(the holy book of Muslims)memorisation and HRV among teenagers.METHODS This experimental study included 16 Tahfiz students and 16 non-Tahfiz students(n=32).The HRV was measured in three tasks:Recalling familiar verses,memorising new verses,and recalling the newly memorised verses of the Quran.HRV analysis was done using these parameters:Standard deviation of N-N(heartbeat peak)interval;low frequency(LF);high frequency(HF)and LF/HF ratio.RESULTS There were significant differences between tasks for all parameters(P<0.05).However,between the groups,only the LF/HF ratio had significant differences,with F=5.04,P<0.05.Pearson correlation showed a moderate positive correlation between the number of pages memorised and the LF/HF ratio(r=0.61,P<0.05).CONCLUSION Quran memorisation increased the HRV and our results suggested that this activity could be developed as an effective sympathovagal modulation training activity.
文摘The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.
基金the National Natural Science Foundation of China（No.30400105）；the National Basic Research Development Program（973）（No. 2003CB716106）；the National Science Fund for Distinguished Young Scholars of China（No.30525030）.
文摘Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.
