ELECTROCARDIOGRAM DATA COMPRESSION TECHNIQUES IN 1D/2D DOMAIN

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.

An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, where the predictor exploits the correlative characteristics of adjacent QRS waveforms. The twin-R correlation prediction and lifting wavelet transform (LWT) for periodical ECG waves exhibits feasibility and high efficiency to achieve lower distortion rates with realizable compression ratio (CR); grey predictions via GM(1, 1) model have been adopted to evaluate the parametric performance for ECG data compression. Simulation results illuminate the validity of our approach.

ECG data compression using the formation of QRS-complex segment bank and integer DCT-based plateau region processing

The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal informations are mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that truncated SVD method can provide an efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission. Index Terms-Data compression, electrocardiogram, feature extraction, quasi-periodic signal, singular value decomposition.

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared. A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses. The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases). The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.

Cardiac diseases constitute the main cause of mortality around the globe. For detection and identification of cardiac problems, it is very important to monitor the patient's heart activities for long periods during his normal daily life. The recorded signal that contains information about the condition of the heart called electrocardiogram (ECG). As a result, long recording of ECG signal amounts to huge data sizes. In this work, a robust lossless ECG data compression scheme for real-time applications is proposed. The developed algorithm has the advantages of lossy compression without introducing any distortion to the reconstructed signal. The ECG signals under test were taken from the PTB Diagnostic ECG Database. The compression procedure is simple and provides a high compression ratio compared to other lossless ECG compression methods. The compressed ECG data is generated as a text file. The decompression scheme has also been developed using the reverse logic and it is observed that there is no difference between original and reconstructed ECG signal.

Objectives: This study was designed characteristics of surface electrocardiogram (ECG) for the localization of septal accessory pathway (AP) in the typical Wolff-Parkinson-White (WPW) syndrome to develop a new algorithm ECG for the septal AP localization, and to test the accuracy of the algorithm prospectively.&#x0D; Subject and Methods: We studied 106 patients, in 65 patients with typical WPW syndrome have a single anterograde with the localization of Aps identified by successful radiofrequency catheter ablation (RCFA) to develop a new ECG algorithm for the septal AP localization. Then this algorithm was tested propectively in 41 patients were compared with the location of AP’s successful ablation by RCFA (gold standard).&#x0D; Results: We found that the 12 lead ECG parameters in 65 patients with typical WPW syndrome such as the transition of the QRS complex, delta wave polarity in V1 lead, delta wave polarity in at least 2/3 inferior leads and morphology QRS was “QRS pattern’’ in inferior leads in diagnosis for the localization of septal APs with hight accuracy predicted from 83.3% to 100%, and for development of a new ECG algorithm. Then the following 41 patients were prospectively evaluated by the new derived algorithm for the septal pathways with high sensitivity and specificity from 84.6% to 100%.&#x0D; Conclusion: 12-lead ECG parameters in typical WPW syndrome closely related to the septal AP localization, in order to develop the new ECG algorithm by parameters as above; and can be used to a new septal ECG algorithm in predicted the location APs with high accuracy predicted

Managing left bundle branch block (LBBB) after transcatheter aortic valve implantation (TAVI) remains challenging. The aim of this study was to develop a novel, simplified electrocardiogram (ECG) algorithm for predicting infranodal conduction delay in LBBB (both new onset as well as preexisting) patients after TAVI and to compare its performance vs current European Society of Cardiology (ESC) ECG criteria. A multicenter analysis of prospectively enrolled patients undergoing electrophysiology testing for preexisting or new-onset LBBB after TAVI was conducted. The novel algorithm was developed by analyzing various combinations of the PR interval, QRS duration pre-TAVI and post-TAVI, and changes in these parameters to identify patients with infranodal conduction delay (defined as a His-ventricular interval ≥70 milliseconds). A total of 769 patients with LBBB (12% preexisting) after TAVI underwent risk stratification using electrophysiology testing at 7 institutions (mean age 81 ± 7 years; 57% female; 21% His-ventricular ≥70 milliseconds). A novel algorithm using solely a PR interval of 190 milliseconds and a QRS interval of 160 milliseconds revealed a sensitivity of 88% and an negative predictive value of 92% for the rule-out of infranodal conduction delay (PR interval post-TAVI <190 milliseconds AND QRS duration post-TAVI <160 milliseconds) and a specificity and positive predictive value of 85% and 41%, respectively, for the rule-in of infranodal conduction delay (PR interval post-TAVI ≥190 milliseconds AND QRS duration post-TAVI ≥160 milliseconds). By comparison, the ESC ECG criteria showed a sensitivity of 72%, a negative predictive value of 88%, specificity of 53%, and a positive predictive value of 28%. The novel, simplified ECG algorithm showed a superior performance for the rule-out and rule-in of infranodal conduction delay compared with current ESC ECG criteria.

This paper presents a technique used to choose optimal wavelets for electrocardiogram (ECG) signal data compression. At present, it is not clear which wavelet function is suitable for data compression of ECG signals. An important issue is that the performance of wavelet based algorithms may depend on the particular basis chosen for the signal compression. Various criteria are used to evaluate the fidelity of the reconstruction. The percent root difference (PRD) has been widely used in the literature as the principal error criterion. In this paper, three more criteria are used, namely, signal to noise ratio (SNR), distortion (D), and root mean square error (RMSE). We use a multiwavelet system that can simultaneously provide perfect reconstruction while preserving length (orthogonality), good performance at boundaries (via linear-phase symmetry), and high order of approximation (vanishing moments). Experimental results are shown for both multiwavelets and scalar wavelets.

Electrocardiogram (ECG) signals are continuously monitored for early diagnosis of heart diseases. However, a long-term monitoring generates large amounts of data at a level that makes storage and transmission difficult. Moreover, these records may be subject to different types of noise distributions resulting from operating conditions. Therefore, an effective and reliable data compression technique is needed for ECG data transmission, storage and analysis without losing the clinical information content. This study proposes the ε-insensitive Huber loss based support vector regression for the compressing of ECG signals. Since the Huber loss function is a mixture of quadratic and linear loss functions, it can properly take into account the different noise types in the data set. Compression performance of the proposed method has been assessed using ECG records from the MIT-BIH arrhythmia database. Experimental results demonstrate that the proposed loss function is an attractive candidate for compressing ECG data.

The electrocardiogram (ECG) is a non-invasive procedure used to diagnose cardiovascular disorders. The 2D ECG system gives important information on the electrophysiology of heart illnesses and possible brain changes, as well as important information about the cardiovascular system’s functional characteristics. The classification proposed system detected four different classes from the ECG cases: myocardial infarction (MI), Arrhythmia, History MI, and Normal case. The suggested system used three steps for classification ECG, include: Image processing, feature extraction, and classification. The first step involves utilizing Image preprocessing methods to prepare and enhance the input images, which includes two steps: converting RGB images to a binary image and applying filters to reduce noise from a binary image. Utilizing local binary pattern algorithm, features are retrieved from the images in the second step (LBP). The collected characteristics are then supplied into a 1D "convolutional neural network" (1D-CNN) model for object detection and classification in the third step. The findings demonstrate that the suggested model of the CNN has a high accuracy rate, with a classification accuracy rate of 97 percent. The suggested method works extremely well in the categorization of the 2D ECG, as demonstrated by the low rate of false alarms and high rate of accuracy.

In healthcare, electrocardiography (ECG) sensors generate a large amount of heart electrical signal that must be efficiently compressed to enable fast data transfer and reduce storage costs. Existing methods for ECG data compression do not fully exploit the characteristics of ECG signals, leading to suboptimal compression. This study proposes a data compression technique for ECG data by exploiting the known characteristics of ECG signals. Our approach combines Savitzky-Golay filtering, detrending, discrete cosine transform, scalar quantization, run-length encoding, and Huffman coding for the effective compression. To optimize the compression performance, we generated quantization intervals tailored to the ECG data characteristics. The proposed method experimentally produces a high compression ratio of 127.61 for a design parameter K = 8, a minimum percentage root mean square difference of 1.03% for K = 128, and a maximum quality score (QS) of 39.78, where K is the number of quantization intervals. Moreover, we compared the proposed method to state-of-the-art methods on a widely used ECG benchmark dataset. We found that the proposed method outperforms the others in terms of the QS, which measures the overall compression-decompression ability. By enabling more storage and faster data transfer, the proposed method can facilitate the widespread use and analysis of large volumes of ECG data, thereby contributing to advances in healthcare.

BackgroundSmartwatches are commonly capable to record a lead-I-like electrocardiogram (ECG) and perform a photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Wearable technologies repeatedly face the challenge of frequent premature beats, particularly in target populations for screening of AF.ObjectiveTo investigate the potential diagnostic benefit of six-lead ECG compared to single-lead ECG and PPG-based algorithm for AF detection of the wrist-worn device.Methods and resultsFrom the database of DoubleCheck-AF 249 adults were enrolled in AF group (n = 121) or control group of SR with frequent premature ventricular (PVCs) or atrial (PACs) contractions (n = 128). Cardiac rhythm was monitored using a wrist-worn device capable of recording continuous PPG and simultaneous intermittent six-lead standard-limb-like ECG. To display a single-lead ECG, the six-lead ECGs were trimmed to lead-I-like ECGs. Two diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as “AF”, “SR”, or “Cannot be concluded”. AF detection based on six-lead ECG, single-lead ECG, and PPG yielded a sensitivity of 99.2%, 95.7%, and 94.2%, respectively. The higher number of premature beats per minute was associated with false positive outcomes of single-lead ECG (18.80 vs. 5.40 beats/min, P < 0.01), six-lead ECG (64.3 vs. 5.8 beats/min, P = 0.018), and PPG-based detector (13.20 vs. 5.60 beats/min, P = 0.05). Single-lead ECG required 3.4 times fewer extrasystoles than six-lead ECG to result in a false positive outcome. In a control subgroup of PACs, the specificity of six-lead ECG, single-lead ECG, and PPG dropped to 95%, 83.8%, and 90%, respectively. The diagnostic value of single-lead ECG (AUC 0.898) was inferior to six-lead ECG (AUC 0.971) and PPG-based detector (AUC 0.921). In a control subgroup of PVCs, the specificity of six-lead ECG, single-lead ECG, and PPG was 100%, 96.4%, and 96.6%, respectively. The diagnostic value of single-lead ECG (AUC 0.961) was inferior to six-lead ECG (AUC 0.996) and non-inferior to PPG-based detector (AUC 0.954).ConclusionsA six-lead wearable-recorded ECG demonstrated the superior diagnostic value of AF detection compared to a single-lead ECG and PPG-based AF detection. The risk of type I error due to the widespread use of smartwatch-enabled single-lead ECGs in populations with frequent premature beats is significant.

Influence of the right ventricular septum/free-wall boundary (hinge) on differentiation of the ventricular lead implantation site