Seizure detection based on heart rate variability using a wearable electrocardiography device

Jeppesen, Jesper; Fuglsang‐Frederiksen, Anders; Johansen, Peter; Christensen, Jakob; Wüstenhagen, Stephan; Tankisi, Hatice; Qerama, Erisela; Heß, Alexander; Beniczky, Sándor

doi:10.1111/epi.16343

Cited by 90 publications

(118 citation statements)

References 34 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…We agree that the equation of HR‐diff can be rewritten as spelled out in the comment. However, we see no quandary in the original equation as it is written in both this and in an earlier publication, since it clearly defined the result. The HR‐diff equation can be written in the way we originally presented it in the papers or rewritten as Widman and colleagues have done.…”

mentioning

confidence: 85%

See 1 more Smart Citation

In response: Heart rate differential method simple but inefficient method for seizure detection

2019

Self Cite

View full text Add to dashboard Cite

To the Editors,We wish to thank Widman and colleagues for their interest in our recent publication on seizure detection based on heart rate variability. 1 We agree that the equation of HR-diff can be rewritten as spelled out in the comment. However, we see no quandary in the original equation as it is written in both this and in an earlier publication, 1,2 since it clearly defined the result. The HR-diff equation can be written in the way we originally presented it in the papers 1,2 or rewritten as Widman and colleagues have done. However, we do not believe that one way is "better" than the other because each is simply just another way of writing the formula. Thus, we see no reason why the HR-diff parameter should not be written as we originally defined it.The HR-diff is a parameter that we stated in 2 : "simply computes the positive rate of heart rate change within the given window." We used this parameter in both papers to compare this simplest form of computing heart rate change (HR-diff) against more advanced heart rate variability methods (ModCSI and CSI).In the comments by Widman and colleagues, they state that: "As could then be expected, this (HR-diff) parameter scores badly (visible in figure 2) while this parameter scores better after prefiltering, which enables the possibility to place information from more than the four remaining arbitrarily chosen R-R intervals (−1;0;k;k + 1) into the parameter."First, seizure detection based on heart rate changes as the simple change in pulse-rate or R-R time-length within a given time-or R-R interval window length has been the method of choice in several papers analyzing seizure detection. Therefore, we opted to assess this aspect too. The explanation for the poor performance of this parameter is probably not related to any physiologic issues but rather to some few misplaced R-peak detections (noise) during baseline data of the patients, which can create too high a threshold for seizure detection in the test data. The algorithm with prefiltering before calculating HR-diff (HR-diff-filtered) confirm this suggestion, as it filters out outliers (noise) and preforms much better than HR-diff without pre-filtering. However, still the more advanced ModCSI and CSI algorithms performs better than any stand-alone HR-diff algorithm with or without prefiltering in our analyses of the test data.Second, the four R-R intervals in the equation are not "arbitrarily" chosen. They have k-length difference (window length: 50 or 100 R-R interval lengths), which, as stated, simply computes the positive rate of heart rate change within the given window. We have never stated that the HR-diff method is using averaging/smoothing as suggested in the comments by Widman and colleagues. CONFLICT OF INTERESTNeither of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. ORCIDJesper Jeppesen https://orcid.org/0000-0002-3095-2040 Sándor Ben...

show abstract

mentioning

confidence: 85%

“…We wish to thank Widman and colleagues for their interest in our recent publication on seizure detection based on heart rate variability …”

mentioning

confidence: 99%

In response: Heart rate differential method simple but inefficient method for seizure detection

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…1 We are concerned because it presents an algebraic quandary in its basic formula, which challenges the interpretations of the results of the "HR-diff" parameter. 1 We are concerned because it presents an algebraic quandary in its basic formula, which challenges the interpretations of the results of the "HR-diff" parameter.…”

mentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

“…We read with much interest the recently published article by Jeppesen and colleagues on seizure detection based on heart rate variability. 1 We are concerned because it presents an algebraic quandary in its basic formula, which challenges the interpretations of the results of the "HR-diff" parameter. This quandary was also present in an earlier publication from this group 2 but was not previously challenged.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Critical remark on the “heart rate differential method"/"HR‐diff" parameter

et al. 2019

View full text Add to dashboard Cite

We read with much interest the recently published article by Jeppesen and colleagues on seizure detection based on heart rate variability. 1 We are concerned because it presents an algebraic quandary in its basic formula, which challenges the interpretations of the results of the "HR-diff" parameter. This quandary was also present in an earlier publication from this group 2 but was not previously challenged.The authors claim to average or sum up the so-called "heart rate differential method" over 50 or 100 (in the formula k) R-R intervals using the following equation:As a result of the subtraction of two R-R intervals in each summand of the formula, all R-R intervals except the first two and last two will disappear mathematically, whatever value they may have:Taking into account that x k ≡ t k − t k+1 , where t k is the time of the k-th heartbeat, we come to the simple derivation:Following the shown simplification, this procedure results in only the total R-R interval change over the given number of about k (50 or 100) heartbeats. This, therefore, does not represent the heart rate variability, as it only shows the cumulative effect over time. This will lead to a different interpretation of the results as an "average" or "smoothing" of many intervals does not occur using this formula (except for the trivial averaging of two successive R-R intervals). As could then be expected, this parameter scores badly (visible in Figure 2 in 1 ) while this parameter scores better after prefiltering, which enables the possibility to place information from more than the four remaining arbitrarily chosen R-R intervals (-1;0;k;k+1) into the parameter. Therefore, the parameter "HR-diff" does not allow relevant smoothing or averaging on its own and only prefiltering enables some averaging/smoothing. Thus this parameter should be simplified as shown above to save some computational power, and the sum-formula should no longer be used as it stands. CONFLICT OF INTERESTNeither of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

show abstract

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital‐based validation study

Jeppesen,

Lin,

Melo

et al. 2024

Epileptic Disorders

Self Cite

View full text Add to dashboard Cite

ObjectiveAutomated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)‐based seizure detection using patient‐adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long‐term video‐EEG monitoring (LTM) in a Brazilian patient cohort.MethodsIctal and inter‐ictal ECG‐data epochs recorded during LTM were analyzed retrospectively. Thirty‐four patients had 107 seizures (79 focal, 28 generalized tonic–clonic [GTC] including focal‐to‐bilateral‐tonic–clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV‐based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient‐adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non‐responders.ResultsThe patient‐adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6–93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty‐five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%).SignificanceThe study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

show abstract

Seizure detection based on heart rate variability using a wearable electrocardiography device

Cited by 90 publications

References 34 publications

In response: Heart rate differential method simple but inefficient method for seizure detection

In response: Heart rate differential method simple but inefficient method for seizure detection

Critical remark on the “heart rate differential method"/"HR‐diff" parameter

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital‐based validation study

Contact Info

Product

Resources

About