# Artificial Intelligence for Detection of Ventricular Oversensing Machine Learning Approaches for Noise Detection Within Non-Sustained Ventricular Tachycardia Episodes Remotely Transmitted by Pacemakers and Implantable Cardioverter Defibrillators

Marc Strik, Benjamin Sacristan, Pierre Bordachar, Josselin Duchateau, Romain Eschalier, Pierre Mondoly, Julien Laborderie, Narimane Gassa, Nejib Zemzemi, Maxime Laborde, et al.

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## ► To cite this version:

Marc Strik, Benjamin Sacristan, Pierre Bordachar, Josselin Duchateau, Romain Eschalier, et al. Artificial Intelligence for Detection of Ventricular Oversensing Machine Learning Approaches for Noise Detection Within Non-Sustained Ventricular Tachycardia Episodes Remotely Transmitted by Pacemakers and Implantable Cardioverter Defibrillators. Heart Rhythm, In press, 10.1016/j.hrthm.2023.06.019. hal-04155080

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Submitted on 1 Oct 2025

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# Artificial Intelligence for Detection of Ventricular
Oversensing

Machine Learning Approaches for Noise Detection Within Non-
Sustained Ventricular Tachycardia Episodes Remotely Transmitted by
Pacemakers and Implantable Cardioverter Defibrillators

Running title: Machine Learning for Detection of Lead Noise

Marc Strik MD PhD¹,², Benjamin Sacristan MD¹,², Pierre Bordachar MD PhD¹,², Josselin Duchateau MD PhD¹,², Romain Eschalier PhD³, Pierre Mondoly MD PhD⁴, Julien Laborderie MD⁵, Narimane Gassa², Nejib Zemzemi PhD², Maxime Laborde², Juan Garrido⁶, Clara Matencio Perabla⁶, Guillermo Jimenez-Perez PhD⁶, Oscar Camara PhD⁶, Michel Haïssaguerre MD¹,², Rémi Dubois PhD², Sylvain Ploux MD PhD¹,²

1. Bordeaux University Hospital (CHU), Cardio-Thoracic Unit, F-33600 Pessac, France

2. IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Université de Bordeaux, F-33600 Pessac- Bordeaux, France

3. Department of Cardiology, University Hospital Clermont-Ferrand, Clermont-Ferrand, France

4. Department of Cardiology, University Hospital Rangueil, Toulouse, France.

5. Department of Cardiology, Bayonne Hospital, Bayonne, France.

6. Universitat Pompeu Fabra, Barcelona, Spain

**Word count:** 4100 words

Address for correspondence: Dr Strik ; Service Pr Haïssaguerre, Hôpital cardiologique du Haut-Lévêque, Avenue de Magellan, 33600 Pessac, France.

e-mail:marcstrik@gmail.com

**Conflict of Interest:** None declared

**Funding:** This work received financial support from the French Government as part of the "Investments of the Future" program managed by the National Research Agency (ANR) [Grant number ANR-10-IAHU-04]
# Abstract

## Background

Pacemakers (PMs) and implantable cardioverter defibrillators (ICDs) increasingly automatically record and remotely transmit non-sustained ventricular tachycardia (NSVT) episodes which may reveal ventricular oversensing.

## Objectives

We aimed to develop and validate a machine learning algorithm which accurately classifies NSVT episodes transmitted by PMs and ICDs in order to lighten healthcare workload burden and improve patient safety.

## Methods

PMs or ICDs (Boston Scientific) from four French hospitals with ≥1 transmitted NSVT episode were split into three subgroups: training set, validation set, and test set. Each NSVT episode was labelled as either physiological or non-physiological. Four machine learning algorithms (2DTF-CNN, 2D-DenseNet, 2DTF-VGG, and 1D-AgResNet) were developed using a training and validation dataset. Accuracies of the classifiers were compared with an analysis of the remote monitoring team of the Bordeaux University Hospital using F2 scores (favoring sensitivity over predictive positive value) using an independent test set.

## Results

807 devices transmitted 10.471 NSVT recordings (82% ICD, 18% PM), of which 87 devices (10.8%) transmitted 544 NSVT recordings with non-physiological signals. The classification by the remote monitoring team resulted in an F2 score of 0,932 (sensitivity of 95%, specificity of 99%) The four machine learning algorithms showed high and comparable F2 scores (2DTF-CNN: 0,914, 2D-DenseNet: 0,906, 2DTF-VGG: 0,863, 1D-AgResNet: 0,791) and only 1D-AgResNet had significantly different labeling as compared with the remote monitoring team.

## Conclusion

Machine learning algorithms were accurate in detecting non-physiological signals within EGMs transmitted by pacemaker and ICDs. An artificial intelligence approach may render remote monitoring less resourceful and improve patient safety.
Key words:

Pacemaker, implantable cardioverter defibrillator, remote monitoring, machine learning, artificial
intelligence, lead noise
## Introduction

Remote monitoring of pacemakers (PM) and implantable cardioverter defibrillators (ICD) has been shown to improve patient outcomes by early detection of technical issues and enjoys a class I recommendation.¹,² Remote monitoring in its current form requires significant human resources trained to extract critical information from the transmitted data.³,⁴ This approach is associated with rising costs and major organizational challenges. As an increasing amount of implanted PMs and ICDs transmit increasing amounts of mostly abundant data, remote monitoring may become the victim of its own success, possibly becoming non-sustainable in the near future. A striking illustration of this dilemma is the analysis of non-sustained ventricular tachycardia (NSVT) electrograms (EGM). The occurrence of successive fast ventricular sensed events triggers classification and recording of NSVT EGMS which are automatically sent to the proprietary RM platform. Contemporary PMs and ICDs RM systems are designed to allow RM notification for each of these NSVT episodes which may be numerous and often of low clinical importance. Careful analysis remains however critical since these episodes occasionally reveal ventricular oversensing which may be related to lead failure, physiological oversensing or electromagnetic interference.⁵ The early identification of ventricular oversensing is crucial for the prevention of clinical complications such as delivery of inappropriate shocks and syncope due to inappropriate inhibition of pacing.⁶,⁷ Artificial intelligence is increasingly used in cardiac signal analysis and may also be able to automatically classify transmitted NSVT episodes through the use of machine learning (ML). A ML algorithm could alert healthcare professionals in case of suspicion of ventricular oversensing. When implemented in remote monitoring, this new approach may lighten the burden of healthcare professionals but also improve patient safety by prioritizing episodes showing ventricular oversensing. Artificial intelligence may therefore aid and speed-up clinical decision-making, avoiding inappropriate therapies and symptomatic lead dysfunction. Our objective was to develop and validate a machine learning algorithm which classifies NSVT episodes remotely transmitted by PMs and ICDs.

## Methods

### Non-sustained ventricular arrhythmia episodes

A wireless transmitter device is first connected to the PM/ICD and then given to the patient to be plugged to a power socket in the vicinity of where he or she sleeps. In case of registration of an EGM due to suspicion of NSVT, the associated episode is transmitted the following night to the proprietary remote monitoring platform as an alert. All PM/ICDs (Boston Scientific) from four French Hospitals (Bordeaux University Hospital, Clermont Ferrand University Hospital, Toulouse University Hospital, Bayonne Hospital), which transmitted at least one NSVT episode to the Latitude remote monitoring
platform were included. No devices were excluded. All NSVT episodes were extracted from the platform and associated EGMs and markers were saved into a single datastructure. Each episode was labelled as either *physiological* (all ventricular markers related to ventricular depolarization) or *non-physiological* (presence of ventricular markers caused by anything other than ventricular deporlirization) by an electrophysiologist. Discrimination between ventricular or supraventricular arrhythmia was not performed; both were considered as *physiological*. The patients (using device serial numbers) were split into three subgroups: training set, validation set, and test set. The training dataset was used to train the model. The validation dataset was used to provide an evaluation of a model fit on the training dataset while tuning model hyperparameters. The test set comprised the sample of data used to provide an unbiased evaluation of the final model. These three data sets were randomly generated from the complete data set. Finally, in order to compare the machine learning algorithms with healthcare professionals, the test set was also analyzed by the remote monitoring nurses of the Bordeaux University Hospital (n=5, each analyzed 20%). The research reported in this paper adhered to the Helsinki Declaration, data were de-identified prior to analysis and the ethics review board of Bordeaux University approved the study under reference CER-BDX 2023 - 46.

## Machine Learning algorithms

Four deep learning (DL) classifiers were tested in order to test three key aspects of the classifier (Figure 1): 1) the input space for handling 1D signals; 2) the imbalanced data-set with missing signals; and 3) the deep network architecture itself. The EGMs were not of equal length and did not always have the same number of channels. A first classifier was designed to input the 1D raw signals as a matrix composed of constant duration, and 3 rows corresponding to the 3 channels right ventricle (RV), right atrial (RA), and Far Field (coil-can). When a channel was not available in a given record, the corresponding row was replaced with zeros. The architecture of the DL classifier was based on the augmented residual network architecture.⁸ This network is subsequently referred to as the 1D-AgResNet. Since deep learning architectures with convolutional layers have shown superiority over other algorithms when dealing with 2D images, a second DL classifier uses as input 2D images representing the trace from the RV channel. The DL architecture used to handle these images is a Dense Net architecture.⁹ This network is referred to as 2D-DenseNet. Fourier transform is known to efficiently analyze noise in temporal recordings. Hence, time/frequency map were computed using a sliding Fourier transform of the RV channel of each recording over a fixed duration window and used as inputs for two last DL classifiers. Then two architectures were designed: a standard Convolutional Neural Network, referred to as 2DTF-CNN and a VGG architecture referred to as 2DTF-Vgg.¹⁰ For each of the 4 proposed classifiers, the hyperparameters (including the optimization algorithm, the class weights to deal with unbalanced classes, and the final number of parameters (i.e., the number of
layers for a given architecture) were optimized using a 5-fold cross-validation scheme on the training
data set. More details on the ML algorithms are found in the Supplemental Material. Once these
parameters were set, several training corresponding to several random initializations of weights were
performed on the entire training set for each of the 4 DL classifiers. The networks showing the best
results on the validation set were selected as the final networks for each DL classifier. The
performances of the DL classifiers were compared on the training set.

## Statistical Analysis

Sensitivity, specificity, positive predictive value and negative predictive value for detecting “non-
physiological” recordings were calculated for each algorithm and the remote monitoring team on the
basis of the “test” data set. An F2 score (the weighted harmonic mean of the precision (positive
predictive value) and recall (sensitivity)) was computed for test comparison. The F2 score gives more
weight to recall than to precision which means a higher score for a lower rate of false negative
(misclassified “non-physiological” recordings). As each machine learning algorithm and the remote
monitoring team labeled the exact same episodes, the McNemar test was performed.¹¹ The
McNemar test evaluates whether the change in labeling proportions in the same set of episodes
across two classifiers are significant. A p-value <0.05 was considered statistically significant. In order
to compare the remote monitoring team capability of detecting non-physiological signals with 95%
sensitivity and 99% specificity to a novel machine learning algorithm using a non-inferiority approach,
with a non-inferiority margin of 5%, and the “disease” prevalence is 5%, the required sample size of
the test dataset should be at least 1112 episodes.

## Data Availability

The datasets generated and analysed during the current study are available from the corresponding
author on reasonable request.

## Results

A total of 807 devices transmitted 10.471 NSVT recordings. 82% of the recordings were transmitted
from ICDs, 18% from pacemakers. The serial numbers were split into three datasets comprising the
training set with 417 devices and 4998 recordings, the validation set with 142 devices and 2621
recordings and finally the test set with 328 devices and 2843 recordings. A flow chart of the devices
and their recordings are shown in **Figure 2**. 87 devices (10.7%) transmitted 544 (5.2%) non-
physiological recordings with a mean number of non-physiological recordings of 6.17 (range 1 - 86).
The final diagnosis for these recordings were: external noise oversensing (379, 69.7%), lead related oversensing (69, 12.7%), diaphragm myopotential oversensing (89, 16.4%) and P-wave oversensing (7, 1.2%). No T-wave oversensing recording was found in the dataset. Examples of physiological and non-physiological recordings are shown in **Figure 3**.

The classification by the remote monitoring team resulted in a sensitivity of 95%, a specificity of 99.3%, and an F2 score of 0,932. The four machine learning algorithms (2DTF-CNN, 2D-DenseNet, 2DTF- VGG, and 1D-AgResNet) showed similar performance as shown by the comparable F2-Scores on the test database for each algorithm in **Figure 4**. The CNN-based network (2DTF-CNN), using time-frequency maps of the ventricular bipolar signal as an input resulted in the highest F2-Score of 0,914. The other algorithms returned a F2-Score of 0,906 for 2D-DenseNet (based on the analysis of 2D images of the ventricular signal), 0,863 for 2DTF-VGG (based on time/frequency maps, pre-trained) and 0,791 for 1D-AgResNet (based on the analysis of 1D signal).

The results of each approach (remote monitoring team and machine learning approaches) in terms of Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are shown in Figure 5. The remote monitoring team had a sensitivity of 95% (90%-98% 95% CI) and a specificity of 99% (98.96%-99.61%, 95% CI). When compared to the remote monitoring team, only for 1D-AgResNet was the agreement in labeling significantly different (p < 0.001, based mainly on the higher amount of false negatives). The contingency tables for each architecture across the three datasets and labeling agreements as compared with the remote monitoring team are shown in the supplemental data.

Discussion

Machine learning can be used to accurately discriminate between physiological and non-
physiological signals in NSVT episodes transmitted by PMs/ICDs through remote monitoring. These
automatic algorithms show diagnostic performance comparable with dedicated remote monitoring
healthcare professionals.

The growing role of artificial intelligence in ECG interpretation

Aiming to facilitate health care decisions and reduce costs, computerized interpretation of the electrocardiogram (ECG) exists since several decades. Limited accuracy of these algorithms based on signal processing require most ECGs to be over read by healthcare professionals. In parallel, there is an exponential use of wearable ECG acquisition technologies, in particular for detection of AF.<sup>12-14</sup> This adds to the ever increasing amount of ECG data which needs to be interpreted. While most
devices are capable of automatic recognition of AF, repeat analysis by a healthcare professional with expertise in ECG rhythm interpretation is still necessary to establish a definitive diagnosis. Accompanied by a growing need in automated ECG interpretation, the arrival of artificial intelligence is on its way to revolutionize the field of automatic interpretation of ECGs.¹⁵,¹⁶ The ability of artificial intelligence and particularly ML to recognize patterns and learn useful features from recorded signals renders it particularly well suited to interpret ECGs and has already been proven to be efficient in diagnosing several ECG conditions including recognition of noise artifacts.¹⁷

## Artificial intelligence in the context of CIED remote monitoring

Remote monitoring of ICDs and but also pacemakers allows for earlier detection of clinical and technical complications and reduces the risk of all cause hospitalization and death.⁴ While these benefits are significant, so are the associated costs. New generation PMs/ICDs automatically transmit large amounts of data and comprehensive interpretation requires a significant amount of human resource and expertise.¹⁸ Despite embedded algorithms designed to alert for specific conditions, the transmitted information has poor positive predictive value, meaning many false positives. At the remote monitoring center of the Bordeaux University Hospital, we follow over 7000 implanted patients which send over a hundred thousand transmissions each year. Less than 4% of these transmissions requires examination by a cardiologist, mean that 96% is filtered by the five remote monitoring healthcare professionals. The conception of our study was therefore driven by a clinical need: to decrease the work burden of the remote monitoring staff. All transmitted NSVT episodes are currently analyzed by healthcare professionals for the purpose of detecting lead oversensing (in ICDs in particular), often manifested by short runs of spurious RR intervals. Although rare, these events are important to identify as they are the cornerstone of inappropriate therapy prevention.¹⁹ NSVT may reveal lead conductor fracture, lead insulation defect, EMI, oversensing of P- R- or T-waves. Per definition, these episodes terminate before binning of the VT/VF counter but may be warning signs of imminent inappropriate shocks. In pacemakers, these episodes often show inappropriate inhibition and may be a warning sign of symptomatic lead dysfunction with possible catastrophic outcome. Two (out of five) device manufacturers do not provide dedicated NSVT alerts on the premise not to overwhelm the healthcare professionals with mostly non-significant events. We hope that ML algorithms as proposed in our study will prioritize transmission of NSVTs in order to improve RM benefits and patient safety.

## Different ML approaches
Four deep network models were tested in this study, with various architectures and input features. In the end, whatever the model, the results are very close, suggesting that deep neural networks are well suited for this classification task. Time frequency maps were first computed from the raw EGMs for the 2DTF-CNN and 2DTF-VGG networks. These maps seemed to be particularly suitable for a noise classification problem. However, this preprocessing step brings little benefit in terms of results compared to a raw processing of the 2D images of the plots. The 1D-agResNet was the only one to use the information of all the available channels. However, this study deals with various types of devices, thus some channels are missing in some configurations (e.g. Coil-Can channel for pacemakers). This network is the one with the lowest results, indicating that the information gained from the additional channels when available is counterbalanced with the complexity introduced by the management of the missing data when the RV is the unique channel. Probably, a training on ICDs only, with the coil-can channel always present, would give better results.

## Future applications/improvements

A major goal of remote monitoring is to decrease inappropriate therapies from sustained oversensing. From clinical practice and literature, we know that they are most often preceded by non-sustained events.⁵ From clinical practice and literature, we know that they are most often preceded by non-sustained events. In a multicenter evaluation of the SecureSense algorithm (Abbott) we found that 95% of the lead failure were detected by non-sustained oversensing events versus 5% for sustained events of oversensing. Our AI approach aims to identify at-risk patients by targeting nonsustained events as those are under analyzed today because of the associated work burden on remote monitoring staff and low yield. Today and in the near future, sustained events are less a target for AI approaches as they are prioritized by the devices and platforms and analyzed by professionals to ensure proper device functioning. This approach could also be applied to other types of transmitted EGMs such as other type of arrhythmia episodes or real-time EGMs. Machine learning could not only be used to detect lead noise but also to improve discrimination between supraventricular and ventricular arrhythmia or other decisions now made by algorithms based on limited rules.²⁰⁻²⁴ The ultimate goal is to entrust the analysis of transmitted EGMs to ML-algorithms in order to maximize the number of patients followed per remote monitoring center.²⁰,²⁴ This would mean that more patients would profit from the benefit of remote monitoring, with a direct effect on the health care system. Alternatively, such algorithms may be used to help healthcare professionals to make a decision. In contemporary devices, the available room for additional algorithms is extremely limited. We do not expect for AI algorithms to become embedded within devices anytime soon. We do expect them to be rapidly integrated within remote monitoring platforms. With the
availability of a continuously improving AI classifier after transmission of each episode, existing onboard algorithms may be changed to work at increased sensitivity.

## Limitations

The non-physiological to physiological episode ratio is highly unbalanced which added to the challenge of the ML algorithms. However, the accuracy of all algorithms was highly satisfactory. The ML algorithms were developed with Boston Scientific tracings of NSVT and may not be directly applicable for the analysis of tracings from a different manufacturer. Because of heterogeneity in the clinical and demographic data, confounding factors may have been missed, such as the higher pre-test probability that ICD wearers transmit true NVST due to underlying cardiac disease.

## Conclusion

Machine learning algorithms were accurate in detecting non-physiological signals within EGMs transmitted by pacemakers and ICDs. An artificial intelligence approach may render remote monitoring more effective and improve patient safety.
References

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## Authors' contributions

Marc Strik, writing - review & editing

Benjamin Sacristan, writing - original draft

Pierre Bordachar, supervision

Josselin Duchateau, data acquisition

Romain Eschalier, data acquisition

Pierre Mondoly, data acquisition

Julien Laborderie, data acquisition

Narimane Gassa, data analysis

Nejib Zemzemi, data analysis

Maxime Laborde, data analysis

Juan Garrido, data analysis

Clara Matencio Perabla, data analysis

Guillermo Jimenez-Perez, data analysis

Oscar Camara, data analysis

Rémi Dubois, data analysis, review & editing

Sylvain Ploux, supervision, review & editing

## role of funding source

This work received financial support from the French Government as part of the "Investments of the Future" program managed by the National Research Agency (ANR) [Grant number ANR-10-IAHU-04] which was used to pay part of the salaries.

## Conflict of interest

The authors report no conflicts of interest.
## Figures

**Figure 1.** An overview of the four deep learning classifiers with 1 to 3 EGM channels as input and diagnosis Physiological or Non-Physiological as output.

**Figure 2.** Flowchart of the patients and NSVT episodes included into the study.

**Figure 3.** Examples of physiologic and non-physiologic recordings.

**Figure 4** – F2-Scores for each algorithm and for the remote monitoring team (test set).

**Figure 5** – Diagnostic performance of each machine learning algorithm and remote monitoring team
1 to 3 EGM leads

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Inputs

Deep Neural Network

Output
Non Sustained Ventricular Tachycardia

807 devices

10471 recordings

Physiological

Non-physiological

771 devices

9927 recordings

87 devices

544 recordings

Training Set

417 devices

4998 recordings

317 Non-physiological

Validation Set

142 devices

2621 recordings

106 Non-physiological

Test Set

328 devices

2843 recordings

121 Non-physiological
N = 9927

<figure><img src="image_6.png" /></figure>

lead related oversensing

N = 69

<figure><img src="image_7.png" /></figure>

diaphragmatic oversensing

N = 89

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

external source oversensing

N = 379

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

P-wave oversensing

N = 7

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>
F2 score

<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /></figure>

* = p < 0,001
versus remote
monitoring team
NSVT episode showing arrhythmia

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

deep
learning
algorithm

<figure><img src="image_19.png" /></figure>

no alert
delivered

NSVT episode showing lead noise

<figure><img src="image_20.png" /></figure>

RV lead noise

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

alert
delivered