Noise-resilient Automatic Interpretation of Holter ECG Recordings

Egorov Konstantin

, Sokolova Elena

, Avetisian Manvel

and Tuzhilin Alexander

Sberbank AI Lab, Moscow, Russia

New York University, New York City, U.S.A.

Keywords:

ECG, Holter, Neural Networks, Segmentation, Classiﬁcation.

Abstract:

Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable

diagnostic information about the patient. Its interpretation becomes a difﬁcult and time-consuming task for

the doctor who analyzes them because every heartbeat needs to be classiﬁed, thus requiring highly accurate

methods for automatic interpretation. In this paper, we present a three-stage process for analysing Holter

recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with encoder-

decoder architecture which detects positions of heartbeats. Second stage is a classiﬁcation NN which will

classify heartbeats as wide or narrow. Third stage in gradient boosting decision trees (GBDT) on top of NN

features that incorporates patient-wise features and further increases performance of our approach. As a part

of this work we acquired 5095 Holter recordings of patients annotated by an experienced cardiologist. A

committee of three cardiologists served as a ground truth annotators for the 291 examples in the test set. We

show that the proposed method outperforms the selected baselines, including two commercial-grade software

packages and some methods previously published in the literature.

1 INTRODUCTION

Cardiovascular diseases remain the leading cause of

death throughout the world, according to the World

Health Organization (WHO) (WHO, 2018). Timely

screening and diagnosis of these diseases can signif-

icantly reduce mortality caused by them. An electro-

cardiogram (ECG) is one of the most affordable and

common tools for recording heart rhythm that facil-

itates the diagnosis of wide range of heart patholo-

gies. Given the fact that heart rhythm abnormalities

and other malfunctions may occur irregularly, mon-

itoring for an extended period of time is necessary

to detect these events. Long-term ECG recordings

(24-hours and more) contain a large amount of valu-

able diagnostic information about the patient, how-

ever their interpretation becomes a difﬁcult and time-

consuming task for the cardiologist who analyzes

them. Holter ECG recordings contain hundreds of

thousands of heartbeats and, ideally, position of each

of them should be determined accurately and each

heartbeat should be classiﬁed individually.

The task of interpretation of long-term Holter

recordings is challenging (Schl

apfer and Wellens,

2017). In one study, computerized interpretation of

ECG signals identiﬁed non-sinus rhythms with accu-

racy of only 53.5% (Shah and Rubin, 2007). Another

study (Lindow et al., 2019) found that among ECGs

with a computer-based diagnosis of atrial ﬁbrillation

or atrial ﬂutter, the diagnosis was incorrect in almost

10%. In almost half of the cases, the misdiagnosis

was not corrected by the over-reading physician. The

clinical impact of the computer-based ECG misinter-

pretation was also evaluated in (Bond et al., 2018)

where it was demonstrated that incorrect automated

diagnosis (AD) signiﬁcantly affects the reader’s inter-

pretation accuracy. In particular, diagnosis accuracies

achieved by cardiology fellows dropped by 43.20%

when an incorrect AD was presented to them.

Duration, amplitude and morphology of QRS

complex (RR interval, width of QRS complex and

slopes of various segments) are important criteria for

detection of abnormal heartbeats (Osowski and Linh,

2001). Therefore, the purpose of our work is to create

an automatic artiﬁcial intelligence algorithm for mak-

ing the key ECG-measurements more precise on the

long-term noisy recordings, such as detection of the

heart beat positions (R-peak) and morphology of QRS

complex (wide or narrow). In this paper, we make the

following contributions. First, we introduce a signal-

wise Convolutional Neural Network (CNN) architec-

ture that operates on the channel level. Second, we

propose a novel method of how to add patient-speciﬁc

208

Konstantin, E., Elena, S., Manvel, A. and Alexander, T.

Noise-resilient Automatic Interpretation of Holter ECG Recordings.

DOI: 10.5220/0010258302080214

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS, pages 208-214

ISBN: 978-989-758-490-9

information to our model by stacking the ECG seg-

mentation and the patient-wise classiﬁcation models

(NN+GBDT).

Third, we show that the proposed method

outperforms the selected baselines, including two

commercial-grade software packages and some meth-

ods previously published in the literature. Finally,

we show that our method approaches the performance

level of experienced cardiologists, which we demon-

strate in an experiment involving 291 annotated ECG

recordings and 3 highly skilled cardiologists.

2 RELATED WORK

Historically, the automated ECG signal interpretation

is implemented by expertly-created feature extraction

algorithms (onset and offset of the different waves,

measurements of various intervals, amplitude param-

eters etc.), while the classiﬁcation is performed by

decision rules, which are also created and tuned by

experts. In order to improve the accuracy of such

methods, a number of machine learning algorithms

were applied to the problem. These methods allowed

use of more informative features, e.g. time to fre-

quency conversion methods (e.g. wavelet-transform)

in order to extract features from variable-length wave-

forms (Essam et al., 2017). However, the problem of

automated interpretation of ECG signals still remains

under-explored, despite all these efforts (Kaplan et al.,

2018)(Estes, 2013). Furthermore, with the advent of

deep-learning based methods, new expectations have

been developed recently that cardiologist-level inter-

pretation of ECGs can be achieved by using modern

deep neural networks (Hong et al., 2019).

One such paper (Shashikumar et al., 2018) de-

scribes how a multi-stage model has been applied to

the atrial ﬁbrillation detection problem. In particular,

the signal was split into 10-minute segments, and the

noise reduction and frequency analysis using wavelet

transform was performed on these signals. Further-

more, features were extracted from the spectrograms

using a CNN, while a BRNN and attention layers

managed to capture temporal patterns in the extracted

features, resulting in the ﬁnal classiﬁcation layer cal-

culating probabilities of predicting atrial ﬁbrillation.

Another work in applying deep learning to the

ECG interpretation task is presented in (Rajpurkar

et al., 2017), where a 34-layer CNN was able to ex-

ceed performance of a cardiologist in detecting a wide

range of heart arrhythmias by leveraging a large an-

notated dataset and a very deep CNN. Our work dif-

fers from (Rajpurkar et al., 2017) as follows. First,

the training and the test sets in the paper were con-

structed in a way to make it more balanced, i.e. taking

only approximately 2 30-seconds windows for each

patient, thus possibly making false positive metric in-

accurate. Second, the paper works only for single-

lead ECG records, while our work focuses on extract-

ing features from multi-channel ECG recordings. The

problem of noise in the ECG recordings is studied in

(Everss-Villalba et al., 2017). First, the authors de-

ﬁned 5 classes of noise, ordered by the clinical im-

pact of the noise on the parameters to be measured

in the ECG. Second, various measures of noise level

have been proposed, such as baseline wander, power-

line interference, and standard deviation noise (which

was proposed in the paper). Third, a noise map is

built, which characterizes temporal distribution of the

noise. The paper demonstrates that noise level can be

prohibitively high for automated or human interpreta-

tion of ECG signals.

Based on this literature review, we conclude that

signiﬁcant progress has been made in addressing the

problem of automated ECG analysis over the last sev-

eral years. However, the problem is far from being

solved due to the complexities associated with having

highly unbalanced datasets and prevalence of signiﬁ-

cant levels of noise in the ECG data.

3 DATA

3.1 Dataset

Data used in the present study was collected from dif-

ferent clinics of one of East European countries. We

worked with a large dataset of ECG recordings con-

sisting of 5,095 ambulatory 2–lead ECG recordings

of 24–hour duration (Table 1 presents the statistics

pertaining to this dataset). The ECG data is sampled

at a frequency of 250 Hz. All the personally iden-

tiﬁable information has been deleted by the clinics.

Each recording was annotated by experienced cardiol-

ogists using commercial ECG analysis software. The

annotation contained positions of individual QRS-

compleses, as well as class labels, such as wide or

narrow QRS-complexes, arrythmia events (extrasis-

toles and pauses) and different types of pauses. Fur-

thermore, these ECG recordings were contaminated

by noisy and unreadable segments or one of the elec-

trodes was disconnected for some time and then re-

connected again. Thus, our task is building a model

which is resilient to such levels of noise.

We split the dataset into the training, validation

and test subsets in the proportion of 74%, 20% and

6% respectively (having 3804, 1000 and 291 records

in each subset). The test set was additionally anno-

Noise-resilient Automatic Interpretation of Holter ECG Recordings

209

Figure 1: The whole algorithm of our approach.

tated by a committee of three independent certiﬁed

and practicing cardiologists, and the ”ground truth”

was determined by the voting of those experts.

Table 1: Dataset Statistics.

Number of records 5 095

Total length 5232 days 21 hours 6 minutes

Total wide QRS count 6 515 633 (1.2 %)

3.2 Preprocessing

In order to reduce noise in the signal, we applied the

following preprocessing steps to our data. First, we

ﬁll signal from disconnected electrodes with linear in-

terpolation between the last non-zero sample and the

ﬁrst non-zero sample after disconnection. The goal of

this step is to remove extreme high-frequency spikes

in the points where the electrodes are disconnected

and reconnected.

Second, we removed the wandering trend and

made isoline close to zero by subtracting two passes

of the mean ﬁlter with a 100-samples window. Third,

we applied a low-pass ﬁlter with the cutoff fre-

quency of 40Hz. The last prepossessing step is down-

sampling with the factor of 2. During the training

step, we also normalized the amplitude and subtracted

the channel-wise median from each training window

to ensure that isoline is as close to zero as possible.

4 Method

4.1 Overview

Our approach consists of three main stages of analysis

(see Fig. 1). First stage is a CNN segmentation model

with the encoder-decoder architecture with a bottle-

neck layer. There are two separate CNN encoders

for each channel with identical structure, but differ-

ent weights. Before bottleneck resulting feature maps

of the encoders are averaged to ensure that both en-

coders generate similar features from different chan-

nels and are supplementary to each other (see Fig. 2).

This decision was based on the fact, that major parts

of some recordings have periods (sometimes up to

100% of time) when one of the electrodes is either

detached or has a very low signal quality index (SQI)

(Li et al., 2008). While in (Shashikumar et al., 2018)

authors facing similar challenge are feeding the neural

network only through the channels with highest SQI,

we found that a more beneﬁcial approach is to use all

channels at all the times, but to let the neural network

learn to distinguish the noisy or absent signals on its

own. Our goal was to create a model that is work-

ing on any number of electrodes, and each attached

electrode enhances the overall performance, while de-

tachment of the electrode does not result in perfor-

mance deterioration. The second stage comprises a

CNN with the structure similar to the ﬁrst stage, but

with the classiﬁcation head instead of segmentation.

Its input consist of the 2-second windows of the sig-

nal centered by position detected in the ﬁrst stage.

Third stage comprises a gradient boosting deci-

sion tree classiﬁer. We use it to enhance the perfor-

mance of the heartbeat classiﬁcation step by incorpo-

rating global patient features into the model. While

the ﬁrst-stage CNN processes only 30 seconds of the

signal and the second-stage CNN processing 2 sec-

onds of signal, we found that it is important to cap-

ture the individual characteristics of a given record-

ing and a patient and use them in ﬁnal classiﬁcation.

It turns out that this step signiﬁcantly improves the

overall classiﬁcation process.

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Figure 2: Neural network of the ﬁrst stage.

4.2 Training

4.2.1 Augmentation

Augmentation is proven to be an efﬁcient technique to

improve the training process and increase the overall

model performance. Our main augmentation is em-

pirically chosen ”channel-wise dropout”: zeroing out

one of the channels entirely with probability of 0.9

and adding strong Gaussian noise instead, thus forc-

ing the model to learn using only the ”worthy” chan-

nels.

The rest of the augmentations we used are com-

mon in the ECG and other signal processing domains

(Salamon and Bello, 2017): adding Gaussian noise to

both channels, resampling by the factor of (0.7 - 1.3)

and channel-wise multiplication by the factor of (0.5

- 1.5).

4.2.2 Sampling and Losses

Due to the extreme imbalance of the wide/narrow

heartbeats in the dataset (almost 1:100), we applied

the positive example upsampling technique during the

training process. It also worth noting that various

losses perform differently with different class bal-

ances. Therefore, we tested three types of losses:

Binary Cross Entropy (BCE) Loss, Focal Loss, and

Dice loss in both the segmentation and the classiﬁca-

tion stages. In our experiments, the best results on the

validation set were achieved with the positive class ra-

tio 3:20 in both stages, while the Dice Loss performed

the best in the segmentation stage and the BCE Loss

in the classiﬁcation stage.

4.2.3 Neural Networks Training

We trained both neural networks for the 100,000 steps

with the batch size of 64. We used Adam optimizer

with B1 = 0.9 and B2 = 0.999, learning rate = 0.001

with exponential decay by the factor 0.97 every 1000

steps. Each batch we formed by sampling wide heart-

beats with probability 0.15 and narrow with probabil-

ity 0.85.

4.2.4 GBTD Training

The third stage classiﬁcation was made by GBDT us-

ing the LightGBM library (Ke et al., 2017). It was

trained on the validation dataset, that was further split

into 700 patients for training and 300 patients for val-

idation in the current stage. Training was stopped

after 461 iterations based on the validation set met-

rics. The feature set for the current stage was gener-

ated using the features obtained in the previous steps.

The most important were the vectors describing each

heartbeat taken from the classiﬁcation network right

after the channel merging step and raw outputs (log-

its) from the network. From these features we gen-

erated patient-wise aggregated features, such as the

mean, the median and the standard deviation of each

Noise-resilient Automatic Interpretation of Holter ECG Recordings

211

Figure 3: Neural network of the second stage.

Table 2: Comparison between Different Methods for the Detection R-peaks task.

Method Sensitivity (Se) Speciﬁcity (+P)

Baseline Vendor 1 0.989 0.975

Baseline Vendor 2 0.968 0.972

Neural Network 0.993 0.990

Cardiologist 0.991 0.992

value. Further, we added features based on the heart-

beat detection from the ﬁrst stage. These are mean,

median rate, local heartbeat rate based on nearest 100

and 10 beats and their relationship with the current

heartbeat.

5 RESULTS

The results of the conducted experiments are shown

in the Table 2. As baselines for comparing our model,

we chose two widely used commercial-grade soft-

ware packages built by two different vendors

. Fur-

thermore, we compare our model with the annota-

tions made by experienced cardiologists over-reading

annotations acquired with the software package pro-

duced by Vendor 2. We measure performance of our

models using Sensitivity (Se) and Speciﬁcity (+P)

metrics. True positive for the detection task was

counted if model detects a heartbeat within 150 ms

of the true one. The comparison is made on the

test dataset consisting of 291 recordings annotated

by a committee of three cardiologists. This dataset

Due to the conﬁdentiality agreements, we cannot reveal

the names of these vendors

was annotated independently from the original train-

ing dataset, which also enables us to evaluate perfor-

mance of a single cardiologist.

As Table 2 demonstrates, our model outperforms

the two selected baselines in the task of detecting po-

sitions of heartbeats, as well as classifying the heart-

beats into narrow and wide. Moreover, the proposed

model achieved the accuracy level comparable to the

experienced cardiologists on these tasks, as shown in

Table 2.

Furthermore, we tested our method on the MIT

BIH Arrhythmia Database and compared the results

of the different approaches described in the literature

with the similar metrics of our model (see Table 3)

(Rodriguez et al., 2014). The MIT-BIH Database is

a test set for evaluation of arrhythmia detection per-

formance as well as for basic research into cardiac

dynamics that has been used about 500 times world-

wide since 1980 (Moody and Mark, 2001). Due to

the fact that this dataset is not suitable for the classi-

ﬁcation task, we evaluated our model only in the con-

text of the detection task. As demonstrated in Table

3, our approach to the challenge of heartbeat detection

showed stronger performance results than the selected

baselines.

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Table 3: Comparison Between Different Methods for the Classiﬁcation QRS task.

Method Sensitivity (Se) Speciﬁcity (+P)

Baseline Vendor 1 0.687 0.961

Baseline Vendor 2 0.318 0.955

Neural Network 0.873 0.997

NN + GBDT 0.917 0.999

Cardiologist 0.872 0.999

Table 4: QRS detection performance comparison on the MIT-BIH arrhythmia database.

Work Recall (%) Precision (%)

Pan and Tompkins (1985) 90.95 99.56

Elgendi et al. (2009) 87.90 97.60

Chouakri et al. (2011) 98.68 97.24

Rodriguez-Jorge et al. (2014) 96.28 99.71

NN + GBDT (our work) 98.11 99.91

6 CONCLUSIONS

In this paper we present a novel heartbeat detection

and heartbeat classiﬁcation (narrow or wide) method

for the two-channel long-term ECGs. We propose a

channel-wise CNN architecture and combine it with

the GBDT model that can employ patient-wise fea-

tures. Furthermore, we demonstrate on the set of 291

ambulatory 2-lead ECG 24-hour recordings that our

method signiﬁcantly outperforms two commercially

available software packages widely used by the car-

diologists for these tasks in the country, approaching

the quality level of experienced radiologists.

As a future work, we intend to conduct prospec-

tive clinical trials for conﬁrmation of clinical signiﬁ-

cance of this model, as well as enhancing our model

for the detection and interpretation of more complex

components of the heartbeat.

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