Multi-lead Electrocardiogram Signals Research Articles

A multi-channel ECG signal deep compressive sensing method using Treeshaped Autoecoder based on multiscale feature fusion

Conventionally, multi-lead Electrocardiogram (ECG) signals are recorded and stored using high sensing rates and high precision, resulting in huge data volumes and placing greater pressure on storage and transmission resources. Compressive sensing (CS) allows efficient encoding and decoding of signals through sparse representation, measurement and reconstruction for compressed storage and transmission of ECG signals. Traditional CS methods generally requires manually selecting the features or dictionaries used in sparse representations, which lacks adaptivity and flexibility, and the complexity of the reconstruction process limits the application of CS in some scenarios with high real-time requirements. In this paper, we propose a deep compressive sensing framework for processing multi-lead ECG signals by combining CS and deep learning, which is based on multi-scale feature fusion to construct a binary tree-shaped autoencoder architecture to achieve efficient compression and reconstruction of ECG signals. Experiments on the PTB diagnostic ECG database show that the proposed method achieves “very good” reconstruction quality at low sensing rates, with PRD and SNR of 1.77 % and 35.67 dB, respectively, when SR = 20 %. In addition, we investigate how the number of quantization bits affects the quality of reconstructed signals. After analysis, we found that 8-bit quantization can be used in practical applications to ensure the quality of reconstructed ECG signals, while decrease the bit rate of the data, improving the transmission efficiency, and reduce energy consumption.

ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVD.

Recently, deep learning (DL) has enabled rapid advancements in electrocardiogram (ECG)-based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead systems based on the potential differences between electrodes placed on the limbs and the chest. When applying DL models, ECG signals are usually treated as synchronized signals arranged in Euclidean space, which is the abstraction and generalization of real space. However, conventional DL models typically merely focus on temporal features when analyzing Euclidean data. These approaches ignore the spatial relationships of different leads, which are physiologically significant and useful for CVD diagnosis because different leads represent activities of specific heart regions. These relationships derived from spatial distributions of electrodes can be conveniently created in non-Euclidean data, making multi-lead ECGs better conform to their nature. Considering graph convolutional network (GCN) adept at analyzing non-Euclidean data, a novel spatial-temporal residual GCN for CVD diagnosis is proposed in this work. ECG signals are firstly divided into single-channel patches and transferred into nodes, which will be connected by spatial-temporal connections. The proposed model employs residual GCN blocks and feed-forward networks to alleviate over-smoothing and over-fitting. Moreover, residual connections and patch dividing enable the capture of global and detailed spatial-temporal features. Experimental results reveal that the proposed model achieves at least a 5.85% and 6.80% increase in F1 over other state-of-the-art algorithms with similar parameters and computations in both PTB-XL and Chapman databases. It indicates that the proposed model provides a promising avenue for intelligent diagnosis with limited computing resources.

MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis from Electrocardiogram.

Automated classification of cardiovascular diseases from electrocardiogram (ECG) signals using deep learning has gained significant interest due to its wide range of applications. However, existing deep learning approaches often overlook inter-channel shared information or lose time-sequence dependent information when considering 1D and 2D ECG representations, respectively. Moreover, besides considering spatial dimension, it is necessary to understand the context of the signals from a global feature space. We propose MD-CardioNet, an efficient deep learning architecture that captures temporal, spatial, and volumetric features from multi-lead ECG signals using multidimensional (1D, 2D, and 3D) convolutions to address these challenges. Sequential feature extractors capture time-dependent information, while a 2D convolution is applied to form an image representation from the multi-channel ECG signal, extracting inter-channel features. Additionally, a volumetric feature extraction network is designed to incorporate intra-channel, inter-channel, and inter-filter global space information. To reduce computational complexity, we introduce a practical knowledge distillation framework that reduces the number of trainable parameters by up to eight times ( from 4,304,910 parameters to 94,842 parameters) while maintaining satisfactory performance compatible with the other existing approaches. The proposed architecture is evaluated on a large publicly available dataset containing ECG signals from over 10,000 patients, achieving an accuracy of 97.3% in classifying six heartbeat rhythms. Our results surpass the performance of some state-of-the-art approaches. This paper presents a novel deep-learning approach for ECG classification that addresses the limitations of existing methods. The experimental results highlight the robustness and accuracy of MD-CardioNet in cardiovascular disease classification, offering valuable insights for future research in this field.

An Attentive Spatio-Temporal Learning-Based Network for Cardiovascular Disease Diagnosis

Automated diagnosis of cardiovascular diseases (CVDs) has become an imperative need for remote or in-hospital heart monitoring. This is a challenging task because of the tenuous morphological variation of the electrocardiogram (ECG) signal across different cardiac diseases. Existing works have attempted to learn the diagnostic representation by capturing the lead-specific morphological variation of a multilead ECG signal. In this work, we have developed an attentive spatio-temporal learning network (ASTLNet) that can learn better diagnostic representation by exploiting the concurrent spatio-temporal variation of a multilead ECG signal. The ASTLNet consists of two modules, i.e., spatio-temporal representation learning (STRL) module and attentive spatio-temporal aggregation (ASTA) module. The STRL module is designed to learn the multiscale spatio-temporal representation, and the ASTA module is designed to aggregate the learned representation. Experiments on the three publicly available datasets, i.e., PTB, PTB-XL, and CPSC-2018, demonstrate that the proposed model can effectively learn the spatio-temporal variation of the ECG signal and gives superior performance compared to the state-of-the-art methods.

An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting arrhythmias for the multi-lead ECG. In this study, we propose an end-to-end multi-label arrhythmia classification model for the 12-lead ECG with varied-length recordings. Our model, called CNN-DVIT, is based on a combination of convolutional neural networks (CNNs) with depthwise separable convolution, and a vision transformer structure with deformable attention. Specifically, we introduce the spatial pyramid pooling layer to accept varied-length ECG signals. Experimental results show that our model achieved an F1 score of 82.9% in CPSC-2018. Notably, our CNN-DVIT outperforms the latest transformer-based ECG classification algorithms. Furthermore, ablation experiments reveal that the deformable multi-head attention and depthwise separable convolution are both efficient in extracting features from multi-lead ECG signals for diagnosis. The CNN-DVIT achieved good performance for the automatic arrhythmia detection of ECG signals. This indicates that our research can assist doctors in clinical ECG analysis, providing important support for the diagnosis of arrhythmia and contributing to the development of computer-aided diagnosis technology.

Spatiotemporal self-supervised representation learning from multi-lead ECG signals

Automatic analysis of electrocardiogram(ECG) signals is one of the applications in the medical domain where deep learning methods demonstrate impressive performance. However, label scarcity remains a critical obstacle limiting the development and clinical application of deep learning methods. Self-supervised pretraining exhibits its potential to alleviate this issue. Motivated by the idea that multi-lead ECG signals gathered simultaneously represent similar underlying cardiac status, we propose ECG-MAE, a novel generative self-supervised pretraining approach. Specifically, an efficient encoder is designed to learn spatiotemporal representations by reconstructing 12-lead ECG signals that have been randomly masked in both time and lead dimensions. Experiments on a multi-label classification problem reveal that our pretrained model obtains a macro-averaged AUC of 0.9474, exceeding prior studies. We observe enhanced downstream performance and label efficiency compared with purely supervised models, allowing us to train more powerful models with the same amount of data and diagnose rare diseases beyond the reach of conventional methods.

A new intelligent ECG recognition approach based on CNN and improved ALO-SVM

Cardiovascular disease is one of the most common diseases, which seriously threatens people’s life and health. Therefore, cardiovascular disease prevention becomes one of the most attractive research topics in health care system design. Intelligent recognition of electrocardiogram (ECG) signals represents an effective method for rapid diagnosis and the evaluation of cardiovascular diseases in medicine. Realization and efficiency of the classification of ECG signals in real time play major roles in the detection of cardiovascular diseases. This paper is concerned with the proposition of an intelligent ECG signal recognition method based on a convolutional neural network (CNN) and support vector machines (SVM) with an improved antlion algorithm (ALO). First, the ECG signal is denoised and pre-processed by lifting the wavelet. Subsequently, CNN is used to extract the signal characteristics of the denoising signal, and the extracted signal characteristics are used as the input characteristics of the SVM. Finally, an improved ALO algorithm is used to optimize the relevant input functions of the SVM to achieve a better signal classification. In our algorithm, the performance is enhanced by improving the threshold estimation method of the lifting wavelet, to improve the filtering effect. The proposed CNN architecture is tested with multi-lead ECG signals from the MIT-BIH ECG signal data set. The results display that the method has obtained an average accuracy, sensitivity, and specificity values of \(99.97\%\), \(99.97\%\), and \(99.99\%\), respectively. Compared with the existing results, the proposed approach has a better recognition performance.

A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG

Objective. Most arrhythmias due to cardiovascular diseases alter the heart’s electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as a diagnostic tool for cardiac abnormality identification. Our objective is to diagnose twenty-nine cardiac abnormalities and sinus rhythm using varied lead ECG signals. Approach. This work proposes a deep residual inception network with channel attention mechanism (RINCA) for twenty-nine cardiac arrhythmia classification along with normal ECG from multi-label ECG signal with different lead combinations. The RINCA architecture employing the inception-based convolutional neural network backbone uses residual skip connections with the channel attention mechanism. The inception model facilitates efficient computation and prevents overfitting while exploring deeper networks through dimensionality reduction and stacked 1-dimensional convolutions. The residual skip connections alleviate the vanishing gradient problem. The attention modules selectively leverage the temporally significant segments in a sequence and predominant channels for multi-lead ECG signals, contributing to the decision-making. Main results. Exhaustive experimental evaluation on the large-scale ‘PhysioNet/Computing in Cardiology Challenge (2021)’ dataset demonstrates RINCA's efficacy. On the hidden test data set, RINCA achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2nd, 5th, 4th, 5th and 4th) for the twelve-lead, six-lead, four-lead, three-lead, and two-lead combination cases, respectively. Significance. The proposed RINCA model is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows RINCA's potential in clinical interpretations.

A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation

Deep learning models developed through multi-lead electrocardiogram (ECG) signals are considered the leading methods for the automated detection of arrhythmia on computer systems. However, due to the amplitudes of input signals, these models generate too many parameters for practical use. Therefore, they are rarely used on devices with limited computational resources in the newly-emerged technology of the Internet of medical things (IoMT). Knowledge distillation was utilized in this paper to propose a method for bridging the gap between the arrhythmia classification model with multi-lead ECG signals and the arrhythmia classification model with single-lead ECG signals by minimizing the performance decline. The proposed method consists of a teacher model with advanced architecture and a student model with simple architecture. The teacher model was already developed through multi-lead ECG signals, whereas the student model was developed through single-lead signals under the supervision of the teacher. Despite its simplicity, the student model receives the dark knowledge of multi-lead ECG signals from the teacher by imitating the teacher’s behavior in the development process. According to the results, the student model was nearly 262.18 times more compressed than its teacher. Moreover, the student experienced approximately 0.81% of accuracy decline in Chapman ECG with 10646 patients.

Automated Localization of Myocardial Infarction of Image-Based Multilead ECG Tensor With Tucker2 Decomposition

Myocardial infarction (MI) causes rapid and permanent damage to the heart muscle. Without timely diagnosis and treatment, it will deteriorate the myocardial structure and function. The precise localization of MI based on 12-lead electrocardiogram (ECG) signals still remains a great challenge.We thus present a novel algorithm for automatically localizing MI from multi-lead ECG signals. The image-based heartbeat tensorization establishes a third-order lead×time×amplitude tensor structure. This image tensor encompasses key information between leads and the correlation within the heartbeat, which are essential for the MI diagnosis. The Tucker2 decomposition based feature extractor automatically extracts the morphological core tensor of the image tensor. The morphological core tensor includes crucial information among three dimensions. Localization of MI is evaluated as a multi-class problem. We use the bagged decision tree for multi-class classification. The 12-lead ECG signals from the benchmark Physikalisch-Technische Bundesanstalt (PTB) database are employed to verify the applicability of the proposed algorithm. The PTB database includes normal ECG, 11 types of MI: anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior and posterior lateral. We demonstrated, with the morphological core features obtained from the image tensor, that 12 categories of ECG signals achieved an total accuracy of 99.67% and F1 score of 0.9997. The area under the receiver operating characteristic curves and precision-recall curves of each kind of ECG signal has been found to be more than 0.88. The proposed algorithm effectively realizes the classification of normal ECG and 11 categories of MI, and our approach of using 12-lead ECG signal herein holds great promise for helping the cardiologists localize MI.

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks.

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic diagnosis of cardiovascular disease and reducing the massive workload of reviewing continuous ECGs. Hence, how to design an appropriate algorithm for objectively evaluating the multi-lead ECG recordings is particularly important. Despite the deep learning methods performing well in many fields, as a data-driven method, it may not be entirely suitable for ECG analysis due to the difficulty in obtaining sufficient data and the low signal-to-noise ratio of ECG recordings. In this study, with the aim of providing an accurate and automatic ECG quality assessment scheme, we propose an innovative ECG quality assessment algorithm based on hand-crafted statistical features and deep-learned spectral features.Methods In this paper, a novel approach, combining the deep-learned Stockwell transform (S-Transform) spectrogram features and hand-crafted statistical features, is proposed for ECG quality assessment. Firstly, a double-input convolutional neural network (CNN) is established. Then, the S-Transform with a novel online augmentation scheme is performed on the multi-lead raw ECG signal received from one input layer to obtain proper time-frequency representation. After that, the CNN with three convolutional layers is employed to extract robust deep-learned features automatically. Simultaneously, the hand-crafted statistical features, including lead-fall, baseline drift, and R peak features, are calculated and fed into another input layer for feature fusion training. Finally, the deep-learned and hand-crafted features are concatenated and further fused by a fully connected layer for quality classification. Furthermore, a log-odds analysis scheme combining with a gradient-based method can localize the abnormal zone in time, frequency, and spatial domains.Results and Conclusion Our proposed method is evaluated on a publicly available database with 10-fold cross-validation. The experimental results demonstrate that the proposed assessment algorithm reached a mean accuracy of 93.09%, a mean F1-score of 0.8472, and a sensitivity of 0.9767. Moreover, comprehensive experiments indicate that the fusion of CNN features and statistical features has complementary advantages and ideal interpretability, achieving end-to-end multi-lead ECG assessment with satisfying performance.

Cardiac Arrhythmia Classification based on the RMS Signal and Cyclostationarity

The goal of this paper is to present a method of classification for cardiac arrhythmias. This method is based on the analysis of the (RMS) signal extracted from the electrocardiogram (ECG) leads and the heart beat cyclostationarity nature. The coefficients of spectral correlation fuction are useful in order to differentiate among the five types of beats: Normal (N), Premature Ventricular Contraction (PVC), Premature Atrial Contraction (APC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). Some simple statistical measures for spectral correlation coefficients are extracted while Principal Component Analysis (PCA) is used to reduce its features. Finally, the classification using Artificial Neural Networks (ANNs) is implemented. Two experiments were performed, one that used a multi-lead ECG signal, and the other that used the RMS signal as input. These experiments showed that when using the RMS signal better results are obtained. This last method led to some specific results: sensitivity, specificity and accuracy indexes of 97.68%, 99.08% and 98.72%, respectively.

An End-to-End 12-Leading Electrocardiogram Diagnosis System Based on Deformable Convolutional Neural Network With Good Antinoise Ability

Electrocardiogram (ECG) is a tool to help judge heart activity. In recent years, the convolutional neural network (CNN) and various deep learning algorithms have been widely used in ECG diagnosis. CNN only considers the local feature. However, the ECG signal is susceptible to noise, and the waveform is complex, making it difficult for existing methods to get a good result. This article presents a novel neural network architecture for ECG diagnosis based on deformable CNN (Deform-CNN). The architecture makes good use of the feature-learning capability of deformable convolution to learn the time-domain and lead characteristics of multilead ECG signals. The proposed end-to-end method can achieve an overall diagnostic accuracy of 86.3% in the 12-lead ECG data of CPSC-2018, with good antinoise ability, which makes the method have a more competitive performance than other deep learning algorithms. The source code is publicly available at https://github.com/HeartbeatAI/Deform-CNN .

Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography

In this article, a quality controlled compression of multilead electrocardiogram (MECG) is proposed, based on tensor analysis, and implemented upon 3D beat tensor of MECG. To reduce computational complications and execution time, a new approach of principal component analysis (PCA), based on 2-mode Tucker Decomposition, is introduced. In order to maintain relevant features of MECG after reconstruction, multi agent supervised learning system (MASLS) based optimal quantization of each fiber of core tensor is introduced, to limit the percentage root mean squared difference (PRD) within a specified value, maintaining high compression ratio (CR). The MASLS is previously trained offline, using features of tensor fibers, along with optimized quantization levels of those fibers, obtained from a particle swarm optimization (PSO), as reference. In addition, to hide patient’s confidential information, steganography is performed within the core tensor followed by generation of a’ secret key’, which is necessary, while decrypting those information during reconstruction. The whole algorithm is implemented on several MECG records, available in PTB Diagnostic ECG database, and compression result is compared by formation of n-beat tensor separately, using ‘n’ number of successive (‘n’ = 5, 10 and 15) beats. After testing on 547 data, average CR of 22, 41.5, 55.4, PRD of 3.62, 4.96, 5.59 and PRD normalized (PRDN) of 3.61, 4.94, 5.57 are achieved for 5, 10, 15-beat tensor, respectively. This proposed algorithm has provided superior result as compared to recently published works on MECG data compression.

Time-frequency approach to ECG classification of myocardial infarction

Electrocardiogram (ECG) analysis is one of the most important techniques to classify myocardial infarction. It is possible to diagnose that the patient may have a heart attack with ST segment elevation or depression in the ECG recordings taken before patient has a myocardial infarction. We propose a method to classify ST segment using time-frequency distribution based features from multi-lead ECG signals. In contrast to many studies in the literature, the proposed method is based on four-class classifcation method and is tested on a large dataset consisting of three different databases, namely MIT-BIH Arrhythmia database, European ST-T database and Long-Term ST database. Among the classification algorithms, the weighted k-NN algorithm achieved the best average performance with accuracy of 94.23%, sensitivity of 95.72% and specificity of 98.15% using Choi-Williams time-frequency distribution features. Meanwhile, the speed of the proposed algorithm is suitable for telemedicine systems.

Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction

Electrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq −7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system.

A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification.

Background: Cardiovascular diseases (CVD) are the leading cause of death globally. Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs efficiently. We propose a multi-task group bidirectional long short-term memory (MTGBi-LSTM) framework to intelligent recognize multiple CVDs based on multi-lead ECG signals. Methods: This model employs a Group Bi-LSTM (GBi-LSTM) and Residual Group Convolutional Neural Network (Res-GCNN) to learn the dual feature representation of ECG space and time series. GBi-LSTM is divided into Global Bi-LSTM and Intra-Group Bi-LSTM, which can learn the features of each ECG lead and the relationship between leads. Then, through attention mechanism, the different lead information of ECG is integrated to make the model to possess the powerful feature discriminability. Through multi-task learning, the model can fully mine the association information between diseases and obtain more accurate diagnostic results. In addition, we propose a dynamic weighted loss function to better quantify the loss to overcome the imbalance between classes. Results: Based on more than 170,000 clinical 12-lead ECG analysis, the MTGBi-LSTM method achieved accuracy, precision, recall and F1 of 88.86%, 90.67%, 94.19% and 92.39%, respectively. The experimental results show that the proposed MTGBi-LSTM method can reliably realize ECG analysis and provide an effective tool for computer-aided diagnosis of CVD.

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. In this study, we propose a multi-lead ensemble neural network (MENN) to distinguish anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) from healthy control (HC) respectively. In the study, three kinds of sub-networks and multi-lead ECG signals are combined, which fully explores the information of ECG signals and improves the classification performance. The algorithm is evaluated on the PTB database by 5-fold inter-subject cross-validation and the sensitivity (Se), specificity (Sp) and area under the curve (AUC) of AMI detection are 98.35%, 97.49%, 97.92%; The Se, Sp, and AUC of IMI detection are 93.17%, 92.02%, 92.60%. The proposed method achieves the state of the art results on both tasks and outperforms the baseline methods. Hence, the proposed method is potential for automatic MI diagnosis.

I-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-Signal Processing

Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-grained reconfigurable arrays (CGRAs) are good candidates for tackling these conflicting objectives, because thanks to their flexibility and high computational density they can efficiently support the computational hot-spots characterizing bio-digital signal processing applications. The interleaved-datapaths (i-DPs) CGRA presented in this letter further leverages the benefits of this architectural paradigm, focusing on ultralow energy operation. Its defining feature is the complex design of its computing cells, which, by embedding multiple i-DPs, allow a high ratio between computing and control logic, effectively speeding up computations and resulting in a marginal impact on the required integrated circuit area. iDPs increase the energy efficiency of up to 33%, with respect to a single-DP alternative, when executing common kernels in the multilead electrocardiogram signal processing field.