Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets

Artificial neural network for atrial fibrillation identification in portable devices

none6siAtrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%–93.0%), 90.2% (CI: 86.2%–94.3%) and 90.8% (CI: 88.1%–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.openMarinucci D.; Sbrollini A.; Marcantoni I.; Morettini M.; Swenne C.A.; Burattini L.Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L

Intelligent Quad Cane with GPS Allocation for Tachyarrhythmia

This paper aims to present the development of an Intelligent Quad Cane with Global Positioning System (GPS) Allocation for Tachyarrhythmia. Heart attack symptoms vary between individual. Usually, senior citizen or cardiovascular patient is not aware of their heartbeat rate. Lack of early warning is the most common reason of the patient die because of a heart attack. Thus, this project offers an early warning for the cardiovascular patient to the charged person. Furthermore, this project is able to analyze and indicate an abnormality in heart rate pulse for the user. Sinus tachycardia and Supraventricular tachycardia was chosen as a research subject because this disease is an early complication of the heart attack. The uses of Global System for Mobile (GSM) and GPS modules in this project can make the charged person receives the notification from the patient via Short Message Service (SMS) which contains the exact location together with patient’s condition. This condition shortens the time of ambulance to arrive

Feasibility of atrial fibrillation detection from a novel wearable armband device

BACKGROUND: Atrial fibrillation (AF) is the world’s most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. OBJECTIVE: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. METHODS: In our 2-step algorithm, we first calculate the R-R interval variability–based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. RESULTS: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. CONCLUSION: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy

Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC

A real-time ppg peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data

A 58 nW ECG ASIC With Motion-Tolerant Heartbeat Timing Extraction for Wearable Cardiovascular Monitoring

An ASIC for wearable cardiovascular monitoring is implemented using a topology that takes advantage of the electrocardiogram's (ECG) waveform to replace the traditional ECG instrumentation amplifier, ADC, and signal processor with a single chip solution. The ASIC can extract heartbeat timings in the presence of baseline drift, muscle artifact, and signal clipping. The circuit can operate with ECGs ranging from the chest location to remote locations where the ECG magnitude is as low as 30 μV. Besides heartbeat detection, a midpoint estimation method can accurately extract the ECG R-wave timing, enabling the calculations of heart rate variability. With 58 nW of power consumption at 0.8 V supply voltage and 0.76 mm[superscript 2] of active die area in standard 0.18 μm CMOS technology, the ECG ASIC is sufficiently low power and compact to be suitable for long term and wearable cardiovascular monitoring applications under stringent battery and size constraints.MIT Medical Electronic Device Realization Cente

Rastreio da fibrilhação auricular através de dispositivos móveis/smartphones

A Fibrilhação Auricular é a arritmia cardíaca mais comum em todo o mundo, afetando milhares de milhões de pessoas, com elevada morbilidade e mortalidade associadas, traduzidas pelo risco aumentado das suas complicações inerentes, como o acidente vascular cerebral e a insuficiência cardíaca. Vários estudos provaram que o tratamento efetivo da FA reduz o risco destes desfechos, realçando, assim, a importância fulcral da deteção precoce deste quadro clínico pela comunidade médica. Sendo esta entidade de difícil diagnóstico, ainda subdiagnosticada e associada a elevados custos relativamente ao tratamento das suas complicações, torna-se necessário a procura de um método de rastreio de baixo custo e acessível, principalmente, à população em risco. Com a revolução tecnológica observada na última década, o aumento dramático no número de utilizadores de smartphones, mesmo dentro da população mais idosa, suscitou uma exploração concomitante de dispositivos e aplicativos médicos auxiliares, nomeadamente direcionados para a deteção e controlo da fibrilhação auricular. Com isto, esta dissertação tem como objetivo verificar a importância/aplicabilidade destes dispositivos num possível método de rastreio desta patologia.Atrial fibrillation is the most common cardiac arrhythmia worldwide, affecting billions of people, with associated morbidity and mortality, who are at risk of their inherent complications such as stroke and heart failure. ´ Several studies have shown that effective treatment of AF reduces the risk of these outcomes, thus highlighting the central importance of early detection of this clinical picture by the medical community. Due the fact this disease is difficult to diagnose and is still underdiagnosed and associated with high costs in relation to the treatment of its complications, it is necessary to search for a low cost screening method that is accessible mainly to the population at risk. With the technological revolution seen in the last decade, the dramatic increase in the number of smartphone users, even within the older population, has led to a concomitant exploration of auxiliary medical devices and applications, particularly for the detection and control of atrial fibrillation. Hereupon, this dissertation aims to verify the importance / applicability of these devices in a possible screening method of this pathology

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society.

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored