Remote monitoring of heart rate and ECG signal using ESP32

2021 ◽  
Author(s):  
Md Abdur Rahman ◽  
Yue Li ◽  
Tanzim Nabeed ◽  
Md Toufiqur Rahman
Keyword(s):  
Heart Rate ◽  
Remote Monitoring ◽  
Ecg Signal

External validation of a heart failure risk prediction model in a remote monitoring cohort submitted to cardiac resynchronization therapy

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
I Cardoso ◽  
M Coutinho ◽  
G Portugal ◽  
A Valentim ◽  
A.S Delgado ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
High Risk ◽  
Remote Monitoring ◽  
Hospital Admissions ◽  
External Validation ◽  
Left Ventricular Ejection ◽  
National Database ◽  
Failure Risk

Abstract Background Patients (P) submitted to cardiac ressynchronization therapy (CRT) are at high risk of heart failure (HF) events during follow-up. Continuous analysis of various physiological parameters, as reported by remote monitoring (RM), can contribute to point out incident HF admissions. Tailored evaluation, including multi-parameter modelling, may further increase the accuracy of such algorithms. Purpose Independent external validation of a commercially available algorithm (“Heart Failure Risk Status” HFRS, Medtronic, MN USA) in a cohort submitted to CRT implantation in a tertiary center. Methods Consecutive P submitted to CRT implantation between January 2013 and September 2019 who had regular RM transmissions were included. The HFRS algorithm includes OptiVol (Medtronic Plc., MN, USA), patient activity, night heart rate (NHR), heart rate variability (HRV), percentage of CRT pacing, atrial tachycardia/atrial fibrillation (AT/AF) burden, ventricular rate during AT/AF (VRAF), and detected arrhythmia episodes/therapy delivered. P were classified as low, medium or high risk. Hospital admissions were systematically assessed by use of a national database (“Plataforma de Dados de Saúde”). Accuracy of the HFRS algorithm was evaluated by random effects logistic regression for the outcome of unplanned hospital admission for HF in the 30 days following each transmission episode. Results 1108 transmissions of 35 CRT P, corresponding to 94 patient-years were assessed. Mean follow-up was 2.7 yrs. At implant, age was 67.6±9.8 yrs, left ventricular ejection fraction 28±7.8%, BNP 156.6±292.8 and NYHA class >II in 46% of the P. Hospital admissions for HF were observed within 30 days in 9 transmissions. Stepwise increase in HFRS was significantly associated with higher risk of HF admission (odds ratio 12.7, CI 3.2–51.5). HFRS had good discrimination for HF events with receiving-operator curve AUC 0.812. Conclusions HFRS was significantly associated with incident HF admissions in a high-risk cohort. Prospective use of this algorithm may help guide HF therapy in CRT recipients. Funding Acknowledgement Type of funding source: None

scholarly journals Remote monitoring of patients with heart failure during the first national lockdown for COVID-19 in France

2021 ◽  
Author(s):  
Sylvain Ploux ◽  
Marc Strik ◽  
Saer Abu-Alrub ◽  
F Daniel Ramirez ◽  
Samuel Buliard ◽  
...  
Keyword(s):  
Heart Failure ◽  
Health Care ◽  
Heart Rate ◽  
Remote Monitoring ◽  
Marked Decrease ◽  
Clinical Status ◽  
Health Care Use ◽  
Physiological Variables ◽  
Patients With Heart Failure ◽  
Vital Parameters

Abstract Background Multiparametric remote monitoring of patients with heart failure (HF) has the potential to mitigate the health risks of lockdowns for COVID-19. Aims To compare health care use, physiological variables, and HF decompensations during one month before and during the first month of the first French national lockdown for COVID-19 among patients undergoing remote monitoring. Methods Transmitted vital parameters and data from cardiac implantable electronic devices were analyzed in 51 patients. Medical contact was defined as the sum of visits and days of hospitalization. Results The lockdown was associated with a marked decrease in cardiology medical contact (118 days before vs 26 days during, -77%, p = 0.003) and overall medical contact (180 days before vs 79 days during, -58%, p = 0.005). Patient adherence with remote monitoring was 84±21% before and 87±19% during lockdown. The lockdown was not associated with significant changes in various parameters, including physical activity (2±1 to 2±1 h/day), weight (83±16 to 83±16 kg), systolic blood pressure (121±19 to 121±18 mmHg), heart rate (68±10 to 67±10 bpm), heart rate variability (89±44 to 78±46 ms, p = 0.05), atrial fibrillation burden (84±146 vs 86±146 h/month), or thoracic impedance (66±8 to 66±9 Ω). Seven cases of HF decompensations were observed before lockdown, all but one of which required hospitalization, versus six during lockdown, all but one of which were managed remotely. Conclusions The lockdown restrictions caused a marked decrease in health care use but no significant change in the clinical status of HF patients under multiparametric remote monitoring. lay summary The first French COVID-19 lockdown had a huge detrimental impact on conventional health care use (-78% in cardiology medical contact). However the lockdown had little impact over the short-term, if any, on vital parameters and the clinical status of patients with heart failure who were adherent to multiparametric remote monitoring. This remote monitoring strategy allowed early identification and home management of most of the heart failure decompensations during the lockdown.

Design of ECG Acquisition System Based on Matlab

2013 ◽  
Vol 462-463 ◽  
pp. 1001-1004
Author(s):  
Xue Wang ◽  
Wen Liang Niu ◽  
Yuan Sheng Liu
Keyword(s):  
Heart Rate ◽  
Cost Savings ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Threshold Selection ◽  
Selection Algorithm ◽  
Absolute Value ◽  
Wave Detection ◽  
Processing Circuit

In order to more accurate detection of ECG signal and cost savings, this paper designed a set of ECG signal acquisition system based on Matlab. The system is composed of hardware and software. Hardware is made up of signal acquisition processing circuit and MPC82G516 microcontroller for AD conversion. The software mainly contains configuring Matlab serial to realize communication with MPC82G516 and uses abundant filter resources of Matlab to remove noise by differential, absolute value, smoothing and threshold selection algorithm of R wave detection, R wave peak provides the basis for calculating the heart rate. This paper uses Matlab friendly user interface for displaying received data to make the data more intuitive and read more easily.

scholarly journals Dataset of raw and pre-processed speech signals

2020 ◽  
Author(s):  
Mohammed Usman ◽  
Zeeshan Ahmad ◽  
Mohd Wajid
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Deep Learning ◽  
Medical Device ◽  
Remote Monitoring ◽  
Speech Signals ◽  
Non Invasive ◽  
Speech Characteristics ◽  
Measure Heart Rate ◽  
Pulse Oxymeter

Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oxymeter. Physiological parameters such as heart rate bear a correlation to speech characteristics of an individual. Hence, there is a possibility to measure heart rate from speech signals using machine learning and deep learning, which would also allow non-invasive, non contact based and remote monitoring of patients. However, to design such a scheme and verify its accuracy, it is necessary to collect speech recordings along with heart rates measured using a medical device, simultaneously during the recording

ECG SIGNAL GENERATION AND HEART RATE VARIABILITY SIGNAL EXTRACTION: SIGNAL PROCESSING, FEATURES DETECTION, AND THEIR CORRELATION WITH CARDIAC DISEASES

2012 ◽  
Vol 12 (04) ◽  
pp. 1240012 ◽  
Author(s):  
GOUTHAM SWAPNA ◽  
DHANJOO N. GHISTA ◽  
ROSHAN JOY MARTIS ◽  
ALVIN P. C. ANG ◽  
SUBBHURAAM VINITHA SREE
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Bradycardia ◽  
Premature Ventricular Contraction ◽  
P Wave ◽  
Ecg Signal ◽  
Qrs Complex ◽  
T Wave ◽  
St Segment ◽  
Cardiac Disorders

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.

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