Abstract
Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.
Highlights
More than 65 million people affected by epilepsy worldwide and 10–50% of patients with drug-resistant epilepsy (DRE) can potentially take advantage of curative epileptic surgery through craniotomy after complete preoperative evaluations [1,2,3]
We propose a Vagus nerve stimulation (VNS) outcome prediction method with random forest (RF) classifier for patients with DRE based on preoperative heart rate variability (HRV) indices
To study if HRV would be a true biomarker for VNS outcome related to abnormal autonomic nerve function of DRE patients, we provided a comparison to a matched normal population and found that 49 indices showed significant differences between DRE patients and healthy controls
Summary
More than 65 million people affected by epilepsy worldwide and 10–50% of patients with drug-resistant epilepsy (DRE) can potentially take advantage of curative epileptic surgery through craniotomy after complete preoperative evaluations [1,2,3]. Vagus nerve stimulation (VNS) therapy is a beneficial option for patients who are not suitable for craniotomy or cannot benefit from craniotomy. As an adjuvant therapy for patients with DRE, VNS has been widely used by more than 100,000 patients worldwide [4]. Studies have shown that VNS can rarely help patients achieve complete seizure freedom. Seizures were reduced by 50% or more in ∼50% of the patients, while about a quarter of patients with DRE do not get any benefit from the VNS therapy [5]. The outcomes of VNS in patients vary greatly. This may be due to the complexity of clinical factors, including etiology syndromes, etiology, and usage of antiepileptic drugs (AEDs) [6]. Presurgical identification of patients who are not suitable for VNS is valuable
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