Botulinum toxin A versus platelet rich plasma ultrasound-guided injection in the treatment of plantar fasciitis: A randomised controlled trial.

BACKGROUND: Platelet-rich plasma (PRP) and botulinum toxin type A (BTX-A) injections have proven effective in clinical trials for plantar fasciitis treatment but have not been directly compared. We aimed to compare clinical outcomes in patients undergoing PRP or BTX-A injections. METHODS: We performed a randomised controlled trial (59 patients; 1-year follow-up) to assess efficacy, using pain and functional scales (VAS, AOFAS Hindfoot-scale and FAAM questionnaire) and fascia thickness reduction, in control and single ultrasound-guided BTX-A or PRP injection groups. RESULTS: The BTX-A group showed better results at 1-month after treatment. Conversely, the PRP injection was more effective in the long-term, with significant pain reduction and functional improvement. Plantar fascia thickness significantly reduced from months 1 and 3 in the PRP and BTX-A groups, respectively. CONCLUSION: PRP and BTX-A injections are effective in patients with plantar fasciitis with BTX-A achieving better short-term pain reduction and PRP better long-term results. LEVEL OF EVIDENCE: Level I; Randomised Controlled Trial.

Flexor carpi radialis brevis: a rare accessory muscle presenting as an intersection syndrome of the wrist.

The flexor carpi radialis brevis (FCRB) is a rare accessory muscle of the forearm and wrist. It is typically asymptomatic, but has been discovered either incidentally during cadaveric studies or at the time of surgery in patients with distal forearm injury. Rarely, the FCRB muscle is associated with pain. We report a patient with wrist pain related to intersection between the tendon of the FCRB muscle and the tendon of the flexor carpi radialis (FCR) muscle, with an associated longitudinal split tear of the FCR tendon, documented by magnetic resonance imaging (MRI). To our knowledge, this is only the second report in the English literature of this intersection syndrome.

A chemostat array enables the spatio-temporal analysis of the yeast proteome.

Observing cellular responses to perturbations is central to generating and testing hypotheses in biology. We developed a massively parallel microchemostat array capable of growing and observing 1,152 yeast-GFP strains on the single-cell level with 20 min time resolution. We measured protein abundance and localization changes in 4,085 GFP-tagged strains in response to methyl methanesulfonate and analyzed 576 GFP strains in five additional conditions for a total of more than 10,000 unique experiments, providing a systematic view of the yeast proteome in flux. We observed that processing bodies formed rapidly and synchronously in response to UV irradiation, and in conjunction with 506 deletion-GFP strains, identified four gene disruptions leading to abnormal ribonucleotide-diphosphate reductase (Rnr4) localization. Our microchemostat platform enables the large-scale interrogation of proteomes in flux and permits the concurrent observation of protein abundance, localization, cell size, and growth parameters on the single-cell level for thousands of microbial cultures in one experiment.

Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project.

Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.

Diagnostic accuracy of MRI for detection of occult instability of type I anterior to posterior pelvic injuries.

Type I Young and Burgess anterior posterior compression (APC) pelvic injuries have been classically managed non operatively due to theoretical integrity of sacroiliac joint ligaments (SIJL), though examination under anesthesia (EUA) has been proven occult mechanical instability in up to 50% of these injuries.  We sought to determine the diagnostic accuracy of magnetic resonance (MRI) for detection of occult instability on APC-I injuries when compared to EUA. METHODS: Diagnostic test study of prospectively recruited patients admitted with APC-I pelvic injuries between 2015 and 2022. All patients consented to participate in this study were subjected to MRI and EUA. The evaluators of each of these tests were blinded.  On MRI evaluation, SIJL were considered compromised when unilateral injury to anterior SIJL was visualized in three or more consecutive images or in bilateral injuries, when injury to the anterior SIJL in two or more consecutive images on each side was observed. Positive EUA was considered a symphyseal diastasis over 25 mm on stress fluoroscopy. Demographic data was collected as recruited and sensitivity, specificity, accuracy, and positive and negative predictive values were calculated. Confidence interval was set at 95%. EUA was considered the gold standard in statistical analysis. RESULTS: A total of 32 patients mean aged 36 (24-61) years were included. Mean symphyseal diastasis at admission was 17.58 (11 - 25) mm. The median time from injury to EUA was 5 (0-21) days. Positive EUA was observed on 20 patients and 25 patients  presented compromised SIJL. MRI presented a sensitivity of 95% (75.13% - 99.87%), specificity of 50% (21.09% - 78.91%), positive-predictive value of 73% (60.61% to 82.93%), negative-predictive value of 87% (48.66% - 98.08%). CONCLUSION: Injury to SIJL on MRI presented an accuracy of 77% (58.29% - 89.64%) for the detection of occult pelvic instability on EUA.

Applications of fluorescence spectroscopy in protein conformational changes and intermolecular contacts.

Emission fluorescence is one of the most versatile and powerful biophysical techniques used in several scientific subjects. It is extensively applied in the studies of proteins, their conformations, and intermolecular contacts, such as in protein-ligand and protein-protein interactions, allowing qualitative, quantitative, and structural data elucidation. This review, aimed to outline some of the most widely used fluorescence techniques in this area, illustrate their applications and display a few examples. At first, the data on the intrinsic fluorescence of proteins is disclosed, mainly on the tryptophan side chain. Predominantly, research to study protein conformational changes, protein interactions, and changes in intensities and shifts of the fluorescence emission maximums were discussed. Fluorescence anisotropy or fluorescence polarization is a measurement of the changing orientation of a molecule in space, concerning the time between the absorption and emission events. Absorption and emission indicate the spatial alignment of the molecule's dipoles relative to the electric vector of the electromagnetic wave of excitation and emitted light, respectively. In other words, if the fluorophore population is excited with vertically polarized light, the emitted light will retain some polarization based on how fast it rotates in solution. Therefore, fluorescence anisotropy can be successfully used in protein-protein interaction investigations. Then, green fluorescent proteins (GFPs), photo-transformable fluorescent proteins (FPs) such as photoswitchable and photoconvertible FPs, and those with Large Stokes Shift (LSS) are disclosed in more detail. FPs are potent tools for the study of biological systems. Their versatility and wide range of colours and properties allow many applications. Finally, the application of fluorescence in life sciences is exposed, especially the application of FPs in fluorescence microscopy techniques with super-resolution that enables precise in vivo photolabeling to monitor the movement and interactions of target proteins.

Artificial intelligence and machine learning in cardiotocography: A scoping review.

INTRODUCTION: Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability. MATERIAL AND METHODS: The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed. RESULTS: We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice. CONCLUSIONS: There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.

Evaluation of a Wearable System for Fetal ECG Monitoring Using Cooperative Sensors.

Fetal electrocardiography (fECG) has gotten widespread interest in the last years as technology for fetal monitoring. Compared to cardiotocography (CTG), the current state of the art, it can be designed in smaller formfactor and is thus suited for long-term and unsupervised monitoring. In the present study we evaluated a wearable system which is based on CSEM's cooperative sensors, a versatile technology that allows for the measurement of multiple biosignals and an easy integration into a garment or patch. The system was tested on 25 patients with singleton pregnancies and an age of gestation ≥ 37 weeks. To reject unreliable fetal heart rate (fHR) estimations, the signal processing algorithm provides a signal quality index. In 12 out of 21 patients available for analysis, a good performance of fHR estimations was obtained with a mean absolute error < 5 bpm and an acceptance rate >70%. However, the remaining 9 patients showed low acceptance rates and high errors. Besides investigating the source of these high errors, future work includes the investigating improved signal processing algorithms, different body positions and the use of dry electrodes. Clinical Relevance - The aim of this work is to develop a wearable system that can be offered in hospitals as an alternative to cardiotocography, or as a home monitoring tool for at risk fetuses, in the era of evolving telemedicine.

Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation.

The increasing complexity and memory requirements of neural networks have been slowing down the adoption of AI in low-power wearable devices, which impose important restrictions in computational power and memory footprint. These low-power systems are the key to obtain 24/7 monitoring systems necessary for the current personalized healthcare trend since they do not require constant charging. In this work, we apply Knowledge Distillation to our previously published convolutional-recurrent neural network for cardiac arrhythmia detection and classification. We show that the resulting network halves the memory footprint (138 K parameters) and the number of operations (1.84 MOp) compared to the baseline. By using Knowledge Distillation, this network also achieves significantly higher accuracy after quantization (increase in overall F1 score from 0.779 to 0.828) and is capable of running into a nRF52832 System-on-Chip from Nordic Semiconductors. This promising result lays the groundwork for deployment on resource-constrained embedded platforms such as micro-controllers of the ARM Cortex-M family, thus potentially enabling continuous detection of cardiac arrhythmias in low-power wearable devices.

Ultra-low-power Physical Activity Classifier for Wearables: From Generic MCUs to ASICs.

In the era of Internet of Things (IoT), an increasing amount of sensors is being integrated into intelligent wearable devices. These sensors have the potential to produce a large quantity of physiological data streams to be analyzed in order to produce meaningful and actionable information. An important part of this processing is usually located in the device itself and takes the form of embedded algorithms which are executed into the onboard microcontroller (MCU). As data processing algorithms have become more complex due to, in part, the disruption of machine learning, they are taking an increasing part of MCU time becoming one of the main driving factors in the energy budget of the overall embedded system. We propose to integrate such algorithms into dedicated low-power circuits making the power consumption of the processing part negligible to the overall system. We provide the results of several implementations of a pre-trained physical activity classifier used in smartwatches and wristbands. The algorithm combines signal processing for feature extraction and machine learning in the form of decision trees for physical activity classification. We show how an in-silicon implementation decreases up to 0.1 µW the power consumption compared to 73 µW on a general-purpose ARM's Cortex-M0 MCU.

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

MQT-TZ: Secure MQTT Broker for Biomedical Signal Processing on the Edge.

Physical health records belong to healthcare providers, but the information contained within belongs to each patient. In an increasing manner, more health-related data is being acquired by wearables and other IoT devices following the ever-increasing trend of the Quantified Self. Even though data protection regulations (e.g., GDPR) encourage the usage of privacy-preserving processing techniques, most of the current IoT infrastructure was not originally conceived for such purposes. One of the most used communication protocols, MQTT, is a lightweight publish-subscribe protocol commonly used in the Edge and IoT applications. In MQTT, the broker must process data on clear text, hence exposing a large attack surface for a malicious agent to steal/tamper with this health-related data. In this paper, we introduce MQT-TZ, a secure MQTT broker leveraging Arm TRUSTZONE, a popular Trusted Execution Environment (TEE). We define a mutual TLS-based handshake and a two-layer encryption for end-to-end security using the TEE as a trusted proxy. We provide quantitative evaluation of our open-source PoC on streaming ECGs in real time and highlight the trade-offs.

Secure Stream Processing for Medical Data.

Medical data belongs to whom it produces it. In an increasing manner, this data is usually processed in unauthorized third-party clouds that should never have the opportunity to access it. Moreover, recent data protection regulations (e.g., GDPR) pave the way towards the development of privacy-preserving processing techniques. In this paper, we present a proof of concept of a streaming IoT architecture that securely processes cardiac data in the cloud combining trusted hardware and Spark. The additional security guarantees come with no changes to the application's code in the server. We tested the system with a database containing ECGs from wearable devices comprised of 8 healthy males performing a standardized range of in-lab physical activities (e.g., run, walk, bike). We show that, when compared with standard Spark Streaming, the addition of privacy comes at the cost of doubling the execution time.

Respiratory and cardiac monitoring at night using a wrist wearable optical system.

Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9breaths-per-minute (MAPE 6.7%) for the breath rate.

FlyLimbTracker: An active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila.

Understanding the biological underpinnings of movement and action requires the development of tools for quantitative measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving D. melanogaster. We show that this approach yields a more than 6-fold reduction in user intervention when compared with fully manual annotation and can be used to annotate videos with low spatial or temporal resolution for a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.

Real-time gait analysis with accelerometer-based smart shoes.

In this paper, we present the evaluation of a new smart shoe capable of performing gait analysis in real time. The system is exclusively based on accelerometers which minimizes the power consumption. The estimated parameters are activity class (rest/walk/run), step cadence, ground contact time, foot impact (zone, strength, and balance), forward distance, and speed. The different parameters have been validated with a customized database of 26 subjects on a treadmill and video data labeled manually. Key measures for running analysis such as the cadence is retrieved with a maximum error of 2%, and the ground contact time with an average error of 3.25%. The classification of the foot impact zone achieves a precision between 72% and 91% depending of the running style. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.

Towards VO2 monitoring: Validation of a heart rate based algorithm.

This article presents and validates a novel algorithm for the continuous monitoring of the VO2 during exercise. The algorithm relies on instantaneous HR measurements to provide a continuous estimation, and can be integrated in a wearable device (e.g., smartwatch, sensor patch). It can be customized by user's main anthropomorphic parameters and automatically learns from newly incoming data recalibrating itself if needed. The system is evaluated against a database of 14 healthy subjects performing various maximal endurance tests. The proposed method provides a VO2 estimation with average RMSE of 4.63 ml/kg/min.

Reconocimiento de agentes reproductores de saberes y prácticas populares en salud. Municipio de Andes 2019 / Recognition of reproductive agents of popular knowledge and practices in health. Municipality of Andes, 2019 / Reconhecimento de agentes reprodutores de saberes e práticas populares em saúde. Município de Andes 2019

Resumen Objetivos: El estudio pretende reconocer la existencia de agentes portadores y reproductores de prácticas y saberes populares en salud, sus dominios y dinámica en el espacio social. Georreferenciar sus dinámicas y establecer su relación con el Plan Básico de Ordenamiento Territorial. Metodología: Estudio cualitativo analítico, que conjuga la geografía crítica y la etnografía. Mediante el uso de técnicas de georreferenciación, -localización y espacialización-, con uso del software QGIS versión 3.6 Nossa. Y mediante el uso de técnicas de entrevistas a profundidad y observaciones documentadas, información que se ordenó y sistematizó por categorías, mediante la implementación del programa ATLAS.ti versión 9. Resultados: Se reconocieron más de 60 agentes y se localizaron 48. Se reconocen dos dominios del saber popular: el mágico religioso, expresado en sobanderos con secreto, y el curanderismo expresado en hierbateros, rezanderos y sobanderos. Se legitiman a través de su presencia en espacios sociales, en sus tres dimensiones: material, al portar una identidad y un dominio, que habitan con objetos; mental, el cual cargan de relaciones y simbolismos; y político, donde reproducen una cadena comercial vinculada a la zona de vocación comercial establecida por el PBOT del municipio. Conclusiones: Los agentes, sus prácticas y saberes se determinan y legitiman, a partir de actos de reproducción social, cultural y política en el territorio como espacio social, y en sus dimensiones, a partir de expresiones culturales, simbolismos y rituales, relaciones materiales a partir del intercambio con objetos y servicios, y su asiento político en las zonas de vocación territorial.

Abstract Objectives: The study aims at recognizing the existence of agents carrying and reproducing popular practices and knowledge in health, their domains and dynamics in the social space and at geo-positioning their dynamics and establishing their relationship with the Basic Plan of Territorial Planning. Methodology: qualitative analytical study that combines critical geography through georeferencing techniques, -localization and spatialization-, using the QGIS Version 3.6 Nossa program, and ethnography through the use of in-depth interview techniques and observations, information that was organized and systematized by categories through the implementation of the ATLAS.ti version 9 program. Results: More than 60 agents were recognized and 48 were located. Two domains of popular knowledge were recognized: the Religious Magic, expressed in massage therapists (sobanderos) with secrecy, and folk medicine (Curanderismo) expressed in shamans, rezanderos and sobanderos. They are legitimized through their presence in social spaces in their 3 dimensions: material by carrying an identity and a domain inhabiting objects; mental which is loaded with relationships and symbolism; and political where they reproduce an overlapping and expanded commercial chain, linked to the area of commercial vocation delimited by the PBOT of the municipality. Conclusions: Agents, their practices and knowledge are determined and legitimized based on acts of social, cultural and political reproduction in the territory as a social space, and in their dimensions based on cultural expressions, symbolisms and rituals, material relationships from the exchange with objects and services and settled politically in the areas of territorial vocation.

Resumo Objetivos: o estudo pretende reconhecer a existência de agentes portadores e reprodutores de práticas e saberes populares em saúde, seus domínios e dinâmica no espaço social. Georreferenciar suas dinâmicas e estabelecer sua relação com o Plano Básico de Ordenamento Territorial. Metodologia: estudo qualitativa analítico, que conjuga a geografia crítica e a etnografia. Através do uso de técnicas de georreferenciação, -localização e espacialização-, com uso do software QGIS versão 3.6 Nossa. E através do uso de técnicas de entrevistas a profundidade e observações documentadas, informação que se arrumou e sistematizou por categorias, através da execução do programa ATLAS.ti versão 9. Resultados: reconhecerem-se mais de 60 agentes e se localizaram 48. Reconhecem-se dois domínios do saber popular: o mágico religioso, expressado em sobanderos (pessoa que sana a través de massagens) com secreto, e o curandeirismo expressado em curandeiros, rezanderos (pessoa que sana a través da oração) e sobanderos. Legitimam-se através de sua presença em espaços sociais, em suas três dimensões: material, ao trazer uma identidade e um domínio, que habitam com objetos; mental, o qual carregam de relaciones e simbolismos; e político, onde reproduzem uma cadeia comercial vinculada à zona de vocação comercial estabelecida pelo PBOT do município. Conclusões: os agentes, suas práticas e saberes se determinam e legitimam, a partir de atos de reprodução social, cultural e política no território como espaço social, e em suas dimensões, a partir de expressões culturais, simbolismos e rituais, relações materiais a partir do intercâmbio com objetos e serviços, e sua posição política nas zonas de vocação territorial.

An autonomous medical monitoring system: Validation on arrhythmia detection.

In this paper, we present a generic platform for autonomous medical monitoring and diagnostics. We validated the platform in the context of arrhythmia detection with publicly available databases. The big advantage of this platform is its capacity to deal with various types of physiological signals. Many pre-processing steps are performed to bring the input information into a uniform state that will be explored by a machine learning algorithm. Since this block plays a crucial role in the entire processing pipeline, three different methods were evaluated for detection and classification of anomalies. The results presented in this work are validated on cardiac beats, where the highest accuracy was obtained on the classification of normal beats (94%). On the other hand, atrial fibrillation and premature ventricular contraction beats were classified with an accuracy of 78%.