Classification Of Electrocardiogram Signals Research Articles

Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks

An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical business by providing new capabilities such as remote patient monitoring and self-monitoring. This research proposed an IoT-based ECG monitoring system that employs a heart rate sensor to generate data and an intelligent hybrid classification algorithm to categorize the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper describes WISE (Wearable IoT-cloud-based health monitoring system), a one-of-a-kind system for real-time personal health monitoring. WISE makes use of the BASN (body area sensor network) technology to provide real-time health monitoring. WISE rapidly transfers data from the BASN to the cloud, and a lightweight wearable LCD may be included to enable quick access to real-time data. This model can address the issue of class imbalance in the ECG dataset, assisting in the development of an IoT-based smart and accurate healthcare system. Pre-processing, feature extraction, and classification are the three steps in any classification technique, whether it is emotion classification or heart health classification. Sensors are used to collect an ECG signal from a person's outside body. The provided ECG signal is first pre-processed using the Butterworth Filtering Method, which effectively reduces noise from the signal. Following pre-processing, the Adaptive Discrete Wavelet Transform technique is used to anticipate the signal's attributes. Lastly, a decision making classification approach based on relational weights is used to determine if the ECG signal is normal or abnormal.

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

Classification of ECG Signals Using Machine Learning Techniques

Cardiovascular diseases are one of the leading causes of mortality in contemporary society. With the growth in the accumulation of medical data, new opportunities have arisen to enhance diagnostic accuracy using machine learning techniques. Heart diseases present symptoms that can be similar to other disorders or be mistaken for signs of aging. Furthermore, diagnosing based on electrocardiogram (ECG) signals can be challenging due to the variability in signal length and characteristics. This article has developed a methodology for classifying ECG signals using the k-Nearest Neighbor (kNN) algorithm and statistical techniques. 9000 ECG signal samples from the PhysioNet database were processed. The signals were normalized to a length of 9000 samples, and relevant features for classification, such as median, standard deviation, skewness, among others, were extracted. Multiple kNN models with different parameters were trained and evaluated on a test set. The models exhibited high performance in classifying normal signals but faced difficulties in correctly classifying signals with arrhythmias. The weighted kNN algorithm demonstrated the best accuracy, although all models showed a tendency to misclassify abnormal signals due to data imbalance. While significant accuracy was achieved in ECG signal classification, there is still room for improvement. Future strategies could involve extracting more relevant features, addressing data imbalance, and fine-tuning model hyperparameters. Integrating domain knowledge from the medical field and advanced signal processing techniques could further enhance classification accuracy. Received: 3 January 2024 / Accepted: 7 April 2024 / Published: 5 May 2024

REAL-TIME PROCESSING STAGES OF ELECTROCARDIOGRAM SIGNAL: A REVIEW

Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.

Enhanced X-wave recognition in ECG signal using max–min thresholds and detection of QRS complex

Due to the advantages of Electrocardiogram (ECG) signals, which are challenging to replicate yet easy to get, ECG-based identification has become a new path in biometric recognition research. These classic feature extraction techniques require Hand-crafted or feature-specific implications. The methods used for selection and integration of features, are time-consuming. The main objective of this study is develop deep learning approach to study the features of ECG data digital characteristics, thus saving a lot of signal pre-processing steps. This research proposed novel technique in X-wave recognition of ECG signal using max-min threshold technique and classification of ECG signal. This signal has been processed for noise removal and normalization. Then this processed signal has been used to recognize X-wave from ECG signal. From recognized X-wave, the ECG signal has been classified using Improved Support Vector Machine (ISVM). The QRS complex has been detected using Stacked Auto-Encoder with Neural Networks (SAENN). The study took raw ECG signals and entropy-based features evaluated from extracted QRS complexes. Exams are based on classifying heart disorders into two, five, and twenty classes. The experimental findings showed that our suggested model attained a high classification accuracy of 97%, precision of 89%, recall of 90%, F-1 score of 88%.

Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method.

Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature. This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases. Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database. The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters. The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.

A Deep Learning Approach for Detecting Atrial Fibrillation using RR Intervals of ECG

Purpose: Atrial Fibrillation (AF) is one of the most common types of heart arrhythmias observed in clinical practice. AF can be detected using an Electrocardiogram (ECG). ECG signals are time-varying and nonlinear in nature. Hence, it is very difficult for a physician to manually perform accurate and rapid classification of different heart rhythms.
 Materials and Methods: In this paper, we propose a method using Discrete Wavelet Transform (DWT) with db6 as the basis function for denoising ECG signal.
 Results: The denoised ECG is smoothened using the Savitzky- Golay filter. Deep learning methods, such as a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (CNN-LSTM) and ResNet18 are used for the accurate classification of ECG signals using Physionet Challenge 2017 database.
 Conclusion: With a 10-fold cross-validation method the model provided overall accuracy of 98.25% with the CNN-LSTM classifier.

Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

Sudden cardiac death remains a significant concern, emphasizing the critical need for intelligent diagnostic techniques in cardiac disease detection. Cardiologic Vascular Diseases (CVD) pose a significant global health challenge, often associated with unhealthy lifestyle habits. Electrocardiogram (ECG) monitoring plays a crucial role in assessing heart function, but its interpretation can be challenging due to signal complexity. This study proposes a novel approach utilizing fused Slantlet-Spatial features alongside Auto Regression (AR) coefficients to classify electrocardiogram (ECG) signals. Leveraging the Slantlet Transform (SLT) for its superior time localization capabilities and the simplicity of AR coefficients, this methodology aims to enhance the accuracy of cardiac illness classification. Nineteen distinct features were extracted from 300 ECG signals, each comprising 512 samples, enriching the classification scheme's efficacy. The fused feature extraction technique demonstrated success in improving overall accuracy. Employing Binary Particle Swarm Optimization (BPSO), irrelevant features were pruned, resulting in a refined feature vector of only 14 relevant features. This reduction in feature dimensionality significantly influenced the classification accuracy. Subsequently, the refined feature vector was introduced to the classification stage, employing four distinct classifiers: Support Vector Machine (SVM) with various kernel functions, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), and Decision Tree (DT). Simulation results revealed the feasibility of achieving an average classification accuracy of 99.67% across ECG signal categories. Notably, the polynomial kernel function-SVM classifier, with only 2-fold cross-validation, yielded optimal performance. This study emphasizes the potential of fused Slantlet-Spatial and AR features in enhancing the accuracy of cardiac disease classification. Moreover, the efficacy of BPSO in feature selection and the superior performance of the SVM classifier highlight promising avenues for further research in intelligent cardiac diagnosis systems. The proposed approach outperforms existing methods and contributes to the early detection of cardiovascular abnormalities, potentially improving patient outcomes.

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Cardiovascular diseases (CVDs) like arrhythmia and heart failure remain the world’s leading cause of death. These conditions can be triggered by high blood pressure, diabetes, and simply the passage of time. The early detection of these heart issues, despite substantial advancements in artificial intelligence (AI) and technology, is still a significant challenge. This research addresses this hurdle by developing a deep-learning-based system that is capable of predicting arrhythmias and heart failure from abnormalities in electrocardiogram (ECG) signals. The system leverages a model that combines long short-term memory (LSTM) networks with convolutional neural networks (CNNs). Extensive experiments were conducted using ECG data from both the MIT-BIH and BIDMC databases under two scenarios. The first scenario employed data from five distinct ECG classes, while the second focused on classifying data from three classes. The results from both scenarios demonstrated that the proposed deep-learning-based classification approach outperformed existing methods.

Recognition and classification of electrocardiogram signal based on convolutional neural network

Nowadays, the prevalence of cardiovascular diseases is increasing, leading to a rise in mortality rates. The electrocardiogram (ECG) is a common way to track activity of heart, some cardiovascular diseases including arrhythmia can be diagnosed with the ECG. However, diagnosing cardiac arrhythmias still heavily relies on the subjective judgment of cardiovascular specialists based on ECG readings. This project proposes a model that utilizes machine learning algorithms for the classification and processing of ECG signals. After collecting relevant data and performing necessary data preprocessing steps, the Convolutional Neural Network (CNN) model is employed for recognizing and classifying. Experimental findings demonstrate that the proposed method performs excellently in classifying various common types of cardiac arrhythmias, yielding high accuracy of over 99%. The method exhibits fast processing speeds, making it suitable for real-time ECG analysis and monitoring. Therefore, the outcomes of this project hold significant clinical relevance in improving the diagnosis of cardiovascular diseases and enhancing patients' quality of life.

12-Lead ECG signal classification for detecting ECG arrhythmia via an information bottleneck-based multi-scale network

The 12-lead electrocardiogram (ECG) is a reliable diagnostic tool for detecting and treating severe cardiovascular conditions like arrhythmia and heart attack. Deep neural networks (DNNs) have achieved higher accuracy in recent years than traditional ECG signal classification task methods. Convolutional neural network (CNN) and Transformer are the two mainstream architectures of DNN, respectively good at extracting local and global features from input data. This paper proposes the multi-scale convolutional Transformer network (MCTnet), an efficient combination of Transformer encoder and CNN for ECG signal classification. MCTnet utilizes the advantages of CNN and self-attention mechanisms to capture potential features in ECG signal accurately. The dual-branch Transformer encoder extracts different-scale feature representations, enabling the capture of both local and global information. Additionally, an information bottleneck method eliminates redundant information and enhances task-relevant information in the learned representations. To evaluate the performance of MCTnet, comprehensive experiments are conducted on three commonly used ECG datasets. The results demonstrate that MCTnet outperforms current deep learning-based models, highlighting its effectiveness in ECG signal classification. It also shows that the performance of the model can be effectively improved by utilizing multi-scale representation learning and information bottleneck.

Enhancing Security in IoMT: A Blockchain-Based Cybersecurity Framework for Machine Learning-Driven ECG Signal Classification

The Internet of Medical Things (IoMT) revolutionizes healthcare, enhances patient care, and optimizes workflows. However, the integration of IoMT introduces concerns related to privacy and security. In addressing these issues and aiming to bolster privacy and data security, this study presents a novel cybersecurity framework based on blockchain (BC) technology. The primary goal is to ensure secure communication among IoMT devices, preventing unauthorized access and tampering with sensitive data. The proposed framework is implemented in a model designed for classifying electrocardiogram (ECG) signals, utilizing two datasets: a Medical Technology Database (MTDB) with a limited sample size and the Massachusetts Institute of Technology–Beth Israel Hospital (MITBIH) dataset with a more extensive sample size. The datasets are subsequently partitioned into training and testing data. Feature extraction and selection are performed using the Pan-Tomkins and genetic algorithms. To enhance security, BC technology is employed to encrypt the test data. Finally, signal classification is performed using the support vector machine (SVM) classifier. Thus, the model trained on the MITBIH dataset outperforms its small data counterpart, achieving an impressive accuracy rate of 99.9%. Additionally, the model exhibits a true positive rate (TPR) and true negative rate (TNR) of 100%, an F-score of 100%, and a positive predictive value (PPV) of 100%.

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs.

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.

Stress-based Classification of Electrocardiogram Signals Before and After Music Therapy using Heart Rate Variability and Machine Learning

The harmful impacts of excessive stress on people’s health have been widely acknowledged, necessitating effective methods for its identification. Recognizing the importance of early stress detection and intervention, this research aims to contribute to the field of healthcare. To achieve this objective, this study classifies electrocardiogram (ECG) signals by assessing physio-psychological states, specifically stress and examines the role of music therapy in alleviating stress. ECG signals, recorded both before and after a music therapy session, were collected. Using signal processing techniques, essential features were extracted from these ECG signals, resulting in a more accurate identification of stress. Additionally, through experimentation and model evaluation, k-nearest Neighbors (KNN) and Classification and Regression Trees (CART) were determined to be the most effective models for this classification. Both models consistently yielded 90% accuracy. These identified extracted features and models are vital to effectively recognizing stress in ECG signals, offering valuable insights for future studies and clinical applications. This research contributes not only to the development of tools for stress detection but also to the understanding of the therapeutic impact of music.

Accelerating deep convolutional neural network on FPGA for ECG signal classification

Abnormal activity of the heart is known as cardiac arrhythmia which must be recognized in earlier stage to prevent sudden death and premature death. The occurrence of arrhythmia increases with age, and it is detected using an electrocardiogram (ECG) signal. Conversely, it is very complex to manually achieve the quick and exact classification due to the complexity, non-linearity and low amplitude of the ECG signal. As a result, the healthcare field requires an automatic system to recognize abnormal heartbeats from a huge amount of ECG records. So, the deep learning algorithm named as Deep Convolutional Neural Network (DCNN) is proposed in this research to analyze the ECG signal on a field-programmable gate array (FPGA). Before performing a classification process, two different processes called signal pre-processing as well as feature extraction are required. For the process of signal pre-processing, the Error Normalised Least Mean Square (ENLMS) algorithm is utilized in our work, and Discrete Wavelet Transform (DWT) technique is performed to take out the relevant features from the ECG signal. Finally, FPGA based one-dimensional DCNN with 3 convolutional layers, 3 pooling layers, and 3 fully connected layers is proposed to classify the signals with proper complex features. The publicly available MIT-BIH arrhythmia and PTB databases are exploited in this research to process ECG signals on the multi-input structure. In addition, different performance parameters like classification accuracy, specificity, sensitivity, and precision are engaged to evaluate the proposed methodology; also, it is compared with different FPGA based existing classifiers. The analysis shows that the proposed design accomplishes 98.6 % classification accuracy on the MIT-BIH arrhythmia database and 99.67 % accuracy on the PTB database, which is 0.304 % higher than a multilayer perception (MLP) and 0.47 % higher than decision-based classifier. Moreover, the proposed FPGA based DCNN accelerator consumes 0.45 mW, 185.426 MHz operation frequency and takes 15 s to complete the classification process.

A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection.

Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time. This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect. Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set. Hence, we thought our system can be used for practical application.

Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare

In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.

A token selection-based multi-scale dual-branch CNN-transformer network for 12-lead ECG signal classification

The timely identification of cardiovascular diseases is critical for effective intervention, with the electrocardiogram (ECG) serving as a pivotal diagnostic tool. Recent advancements in deep learning-based methods have significantly enhanced the accuracy of ECG signal classification. In clinical settings, cardiologists rely on diagnoses derived from standardized 12-lead ECG recordings. It must be acknowledged that there is considerable redundancy in the 12-lead ECG recordings used for ECG signal classification, thereby hindering their generalization capabilities. Meanwhile, considering multi-scale features in 12-lead ECG recordings is a crucial aspect that is often overlooked by existing methods. Based on the above observations, we develop a multi-scale Convolutional Transformer network for ECG signal classification. By utilizing learnable Convolutional neural network (CNN) blocks and novel dual-branch Transformer encoders, the proposed network automatically extracts features at different scales, resulting in superior feature representation. Additionally, by discarding low-importance patches and focusing on high-importance patches, we effectively alleviate information redundancy in the 12-lead ECG recordings. We conduct comprehensive experiments on three commonly used ECG datasets. The Research results show that our proposed network outperforms existing state-of-the-art networks in multiple tasks.

The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals

Abstract Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.