-An efficient ECG (Electrocardiogram) data compression algorithm called KPDEC (key point detection and error compensation) is presented in this pa-Per. With tkis KPDEC method only the key points (KPs) of ECG signals a...-An efficient ECG (Electrocardiogram) data compression algorithm called KPDEC (key point detection and error compensation) is presented in this pa-Per. With tkis KPDEC method only the key points (KPs) of ECG signals are con-sidered to be saved to make the compression more efficient. These KPs can be ex-tracted from ECG samples by calculating the second-ordered central difrerences.Then an error pre-correcting technique is used to let the saved sample having a rea-sonable compensation berore it is stored. This technique is able to reduce the PRD (Percentage Root Mean Square Difference) obviously. In the paper we describe an optimal cording sckeme for getting higer compression rate. Furthermore, an adap-tive filtering tecknique is designed for reconstructed ECG signals to get better fi-delity waves. The algorithm is able to compress ECG data to 168 bits per second with PRD less than 3%.展开更多
With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-base...With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.展开更多
文摘-An efficient ECG (Electrocardiogram) data compression algorithm called KPDEC (key point detection and error compensation) is presented in this pa-Per. With tkis KPDEC method only the key points (KPs) of ECG signals are con-sidered to be saved to make the compression more efficient. These KPs can be ex-tracted from ECG samples by calculating the second-ordered central difrerences.Then an error pre-correcting technique is used to let the saved sample having a rea-sonable compensation berore it is stored. This technique is able to reduce the PRD (Percentage Root Mean Square Difference) obviously. In the paper we describe an optimal cording sckeme for getting higer compression rate. Furthermore, an adap-tive filtering tecknique is designed for reconstructed ECG signals to get better fi-delity waves. The algorithm is able to compress ECG data to 168 bits per second with PRD less than 3%.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(71/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR29).
文摘With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.
