Page 75 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
P. 75
ong deep learning baseline for single lead ECG
processing
BOTOS Csaba HAKKEL Tamás
Pázmány Péter Catholic Pázmány Péter Catholic
University University
1083 Práter utca 50/A 1083 Práter utca 50/A
Budapest, Hungary Budapest, Hungary
botos.csaba@hallgato.ppke.hu hakkel.tamas@hallgato.ppke.hu
∗ † ‡
GODA Márton Áron REGULY István Z. HORVÁTH András
Pázmány Péter Catholic Pázmány Péter Catholic Pázmány Péter Catholic
University University University
1083 Práter utca 50/A 1083 Práter utca 50/A 1083 Práter utca 50/A
Budapest, Hungary Budapest, Hungary Budapest, Hungary
goda.marton.aron@itk.ppke.hu reguly.istvan@itk.ppke.hu horvath.andras@itk.ppke.hu
ABSTRACT 1. INTRODUCTION
Objective: Atrial fibrillation (AF) is one of the most common Cardiovascular diseases are responsible for the highest per-
serious abnormal heart rhythm conditions, and the number centage of fatal outcomes among health problems in the
of deaths related to atrial fibrillation has increased by an modern world. One of the most common and serious ab-
order of magnitude in the past decades. We aim to create a normal heart rhythm conditions is atrial fibrillation, which
system, which can provide help for cardiologist, classifying affects about 2% to 3% of the population in Europe and
and highlighting important segments in recordings. North America [1]. It is associated with an increased risk of
heart failure, dementia, and stroke. Additionally, the num-
Approach: In this paper, we propose a novel approach for ber of deaths related to atrial fibrillation has increased by an
AF detection using only a deep neural architecture with- order of magnitude in recent decades: growing from 29,000
out any traditional feature extractor for real-time automated in 1990 up to 193,300 in 2015. Researchers project that
suggestions of possible cardiac failures that can detect class by 2030 cardiovascular diseases will account for more than
invariant anomalies in signals recorded by a single channel three-quarters of deaths worldwide [2].
portable ECG device.
While it is essential to develop efficient algorithms to autom-
Results: Detecting the four categories: Normal, AF, Other atize detection for monitoring patients with small portable
and Noisy in terms of the official, F1 metric of hidden dataset or wearable devices, and promising methods [3, 4] are al-
maintained by the organizers of PhysioNet Computing in ready available, there is still no completely satisfying solu-
Cardiology Challenge 2017, our proposed algorithm has scored tion due to the low signal-to-noise ratio of portable ECG
0.88, 0.80, 0.69, 0.64 points respectively, and 0.79 on average. devices, as well as the multiple types and the episodic man-
ner of atrial fibrillation. Unfortunately, detecting atrial fib-
Keywords rillation poses a significant challenge even for the most ex-
perienced cardiac exerts. As a result, a larger time window
deep learning, residual network, fully convolutional network, has to be recorded and examined by experts to arrive at a
time-series, signal processing, ECG, atrial fibrillation, AF diagnosis.
detection
To promote the solution and draw the attention of the scien-
∗Corresponding author. tific community to this problem, a challenge was introduced
†Corresponding author. by PhysioNet [5], which targets the algorithmic classification
‡Corresponding author. of atrial fibrillation signals. In this challenge, 8528 short,
single-channel recordings were provided produced by a low-
cost, portable device called KardiaMobile, manufactured by
AliveCor Inc. [6]. The length of the recordings ranged from
9.0 seconds to 61.0 seconds with an average of 32.5 seconds.
These samples were divided into four different classes: atrial
fibrillation, normal, noisy signals, and recordings from pa-
tients with other cardiac diseases, consisting of 771, 5154,
46, and 2557 samples, respectively.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.75-83 75
Koper, Slovenia, 10 October
processing
BOTOS Csaba HAKKEL Tamás
Pázmány Péter Catholic Pázmány Péter Catholic
University University
1083 Práter utca 50/A 1083 Práter utca 50/A
Budapest, Hungary Budapest, Hungary
botos.csaba@hallgato.ppke.hu hakkel.tamas@hallgato.ppke.hu
∗ † ‡
GODA Márton Áron REGULY István Z. HORVÁTH András
Pázmány Péter Catholic Pázmány Péter Catholic Pázmány Péter Catholic
University University University
1083 Práter utca 50/A 1083 Práter utca 50/A 1083 Práter utca 50/A
Budapest, Hungary Budapest, Hungary Budapest, Hungary
goda.marton.aron@itk.ppke.hu reguly.istvan@itk.ppke.hu horvath.andras@itk.ppke.hu
ABSTRACT 1. INTRODUCTION
Objective: Atrial fibrillation (AF) is one of the most common Cardiovascular diseases are responsible for the highest per-
serious abnormal heart rhythm conditions, and the number centage of fatal outcomes among health problems in the
of deaths related to atrial fibrillation has increased by an modern world. One of the most common and serious ab-
order of magnitude in the past decades. We aim to create a normal heart rhythm conditions is atrial fibrillation, which
system, which can provide help for cardiologist, classifying affects about 2% to 3% of the population in Europe and
and highlighting important segments in recordings. North America [1]. It is associated with an increased risk of
heart failure, dementia, and stroke. Additionally, the num-
Approach: In this paper, we propose a novel approach for ber of deaths related to atrial fibrillation has increased by an
AF detection using only a deep neural architecture with- order of magnitude in recent decades: growing from 29,000
out any traditional feature extractor for real-time automated in 1990 up to 193,300 in 2015. Researchers project that
suggestions of possible cardiac failures that can detect class by 2030 cardiovascular diseases will account for more than
invariant anomalies in signals recorded by a single channel three-quarters of deaths worldwide [2].
portable ECG device.
While it is essential to develop efficient algorithms to autom-
Results: Detecting the four categories: Normal, AF, Other atize detection for monitoring patients with small portable
and Noisy in terms of the official, F1 metric of hidden dataset or wearable devices, and promising methods [3, 4] are al-
maintained by the organizers of PhysioNet Computing in ready available, there is still no completely satisfying solu-
Cardiology Challenge 2017, our proposed algorithm has scored tion due to the low signal-to-noise ratio of portable ECG
0.88, 0.80, 0.69, 0.64 points respectively, and 0.79 on average. devices, as well as the multiple types and the episodic man-
ner of atrial fibrillation. Unfortunately, detecting atrial fib-
Keywords rillation poses a significant challenge even for the most ex-
perienced cardiac exerts. As a result, a larger time window
deep learning, residual network, fully convolutional network, has to be recorded and examined by experts to arrive at a
time-series, signal processing, ECG, atrial fibrillation, AF diagnosis.
detection
To promote the solution and draw the attention of the scien-
∗Corresponding author. tific community to this problem, a challenge was introduced
†Corresponding author. by PhysioNet [5], which targets the algorithmic classification
‡Corresponding author. of atrial fibrillation signals. In this challenge, 8528 short,
single-channel recordings were provided produced by a low-
cost, portable device called KardiaMobile, manufactured by
AliveCor Inc. [6]. The length of the recordings ranged from
9.0 seconds to 61.0 seconds with an average of 32.5 seconds.
These samples were divided into four different classes: atrial
fibrillation, normal, noisy signals, and recordings from pa-
tients with other cardiac diseases, consisting of 771, 5154,
46, and 2557 samples, respectively.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.75-83 75
Koper, Slovenia, 10 October
