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A.A. Pulavskyi, S.S. Krivenko, S.A. Krivenko, I.V. Linskiy, M.F. Posokhov, L.S. Kryvenko
Scientific Company KOLIBRI LLC, Kharkiv National University of radioelectronics, Institute of Neurology, Psychiatry
and Narcology of the National Academy of Medical Sciences of Ukraine, Kharkiv National Medical University
Automatic recognition of
congestive heart failure signs
in heart rate variability data
presented at MECO’2022 and CPSIoT’2022, Budva, Montenegro
www.mecoconference.me
Outline
⚫PROBLEM DEFINITION
⚫HANDCRAFTED FEATURES
⚫DATABASES AND PREPROCESSING
⚫MODEL AND DISCUSSION OF RESULTS
⚫CONCLUSIONS
MECO&CPSIoT’2022, Budva, Montenegro
Problem definition
⚫Cardiovascular diseases are still the leading cause of death and
disability according to WHO
⚫Using of telemedicine devices by laypersons poses new
challenges for their developers
⚫When working with an ECG signal, the analysis of the R-peak
sequence comes to the fore (IBI - interbeat interval sequence)
⚫The division of patterns into physiologically normal and
pathological becomes more accurate since it is based on heart
rate variability (HRV) originated from IBI analysis
⚫The purpose of this study was to create a highly specific
diagnostic model capable of automatically identifying patients
with congestive heart failure (without the involvement of a
professional)
MECO&CPSIoT’2022, Budva, Montenegro
Handcrafted features
Heart rate variability is calculated as the time difference of
successive heartbeats on an ECG. The optimum is the
difference between the P-peaks, but since the P-peak is often
poorly differentiable and subject to noise, the R-peak
difference is considered. That is the time component of HRV.
To find differentiating characteristics, we have used five
symbolic analysis methods based on HRV:
⚫Ordinal patterns (op)
⚫Delta coding (dc)
⚫Binary delta coding (dcbin)
⚫Sigma coding (sc)
⚫Max-min method (mmc)
MECO&CPSIoT’2022, Budva, Montenegro
Signal Preprocessing
The preprocessing of the ECG segment included:
⚫Notch filters at 50 and 60 Hz
⚫Bandpass filter of 0.5–45 Hz
⚫A standard QRS detector has been used to obtain R-
peaks (raw IBI)
MECO&CPSIoT’2022, Budva, Montenegro
Databases
⚫Kaggle ECG Database
⚫BIDMC Congestive Heart Failure Database
⚫Congestive Heart Failure RR Interval Database (CHFRR)
⚫MIT-BIH Normal Sinus Rhythm Database (MBN)
⚫Combined measurement of ECG, Breathing and Seismocardiograms
(CEBS)
⚫Fantasia Database
⚫PTB Diagnostic ECG Database
MECO&CPSIoT’2022, Budva, Montenegro
Results
MECO&CPSIoT’2022, Budva, Montenegro
Databases
Test set MBN+CHFRR CEBS Fantasia PTB
Metric
Sen Spec Sen Spec Spec Spec Spec
Model
0.71 0.99 0.86 0.96 1.00 0.97 0.78
Conclusions
⚫A highly specific LightGBM model of automatic
screening for congestive heart failure was obtained
during the study.
⚫The model's sensitivity was not less than 0.71, and the
specificity was not less than 0.95 on test databases.
⚫Automatic screening implies the determination of
signs of the disease without manual error correction
of RR intervals, including the correction of ectopic
beats, long or short beats, missed beats, extra beats.
⚫The proposed approach can be used for personal
telemedicine devices in the early detection of health
threats.
MECO&CPSIoT’2022, Budva, Montenegro
THANK YOU
Q&A
Liudmyla Kryvenko
info@kolibri.one
MECO&CPSIoT’2022, Budva, Montenegro