Heart Rhythm Analysis, Seizure Detection and Prediction Using a Textile-Based Wearable Sensor in Patients With Epilepsy
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 40
- 试验地点
- 1
- 主要终点
- Mean heart rate by recording state
研究概览
简要总结
The goal of this observational study is to understand heart rhythm changes in adults with epilepsy. It will also explore whether computer programs can use heart recordings to detect or predict seizures.
The study will include 20 adults with temporal lobe epilepsy and 20 healthy volunteers. Participants with epilepsy will have ongoing seizures despite treatment with at least two suitable medicines. All participants will be 18 years or older.
In temporal lobe epilepsy, seizures start in a specific region of the brain. These seizures may spread to both sides of the brain, causing muscle stiffening and rhythmic jerking. Researchers will focus on these spreading seizures when developing seizure detection and prediction methods.
The main questions are:
- How do heart rate and heartbeat timing differ between people with and without epilepsy?
- How do these measures change before, during, and after seizures?
- Can computer programs learn patterns in heart recordings to detect seizures or predict them before they start?
Researchers will compare heart rate and heart rate variability between the two groups. Heart rate variability describes changes in the time between one heartbeat and the next. They will compare recordings during rest, wakefulness, and sleep.
Participants will:
- Wear a fabric chest band that records the heart's electrical activity.
- Have brain activity recorded through sensors on the scalp, alongside video recording.
- Rest quietly while awake for one hour during the recording period.
Participants with epilepsy will have recordings during their usual hospital monitoring. Healthy volunteers will have 24 hours of recording at the same hospital.
Researchers will use the brain recordings and video to identify when seizures happen. They will develop and test computer programs that learn patterns in the heart recordings.
详细描述
This prospective, single-center observational study will characterize heart rate and heart rate variability (HRV) in adults with drug-resistant temporal lobe epilepsy and explore their use in detecting and predicting focal to bilateral tonic-clonic seizures. The study combines comparisons between epilepsy participants and healthy volunteers with analyses of physiological changes surrounding seizures and development of computational models.
The planned sample comprises 20 adults with epilepsy and 20 healthy volunteers. Recruitment will use non-probability sampling. Epilepsy participants will be recruited from patients admitted for clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Healthy volunteers will be recruited through the researchers' university and professional networks, with age and sex distributions as similar as feasible to those of the epilepsy group.
Recordings will take place at Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital. A textile-based wearable chest band will record electrocardiography (ECG) simultaneously with video-EEG. Epilepsy participants will be recorded during their routine inpatient monitoring, while healthy volunteers will undergo 24 hours of recording at the same hospital. Similar equipment and recording conditions will be used in both groups to reduce methodological differences related to the recording environment, physical burden and comfort.
Both groups will complete a standardized one-hour awake resting recording on the first morning, between 09:00 and 12:00, following a 15-minute adaptation period. The timing of the last meal and caffeine and nicotine use will be recorded. Neurologists will review video-EEG recordings from both epilepsy participants and healthy volunteers to identify sleep and wakefulness. In epilepsy participants, they will evaluate interictal epileptiform discharges, seizure onset and offset, seizure classification and postictal EEG changes. In healthy volunteers, they will verify the absence of electrographic seizure activity during the recording. Analyses by sleep stage will be limited to periods that can be reliably classified.
Heart rate and HRV will be compared between groups during standardized rest, sleep and wakefulness. Within epilepsy participants, analyses will examine interictal, preictal, ictal and postictal changes while accounting for repeated observations from the same participant. Medication type, dose and timing relative to the recordings will also be considered in the analysis and interpretation of heart rate and HRV findings. Effect sizes and confidence intervals will accompany the comparisons.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Epilepsy group:
- •Age 18 years or older.
- •Diagnosis of temporal lobe epilepsy and admission to the epilepsy monitoring unit for video-electroencephalography (video-EEG) monitoring.
- •Ongoing seizures despite treatment with at least two appropriately selected and tolerated antiseizure medicines at adequate doses and for adequate durations.
- •History or clinical suspicion of focal to bilateral tonic-clonic seizures.
- •Provision of written informed consent.
- •Healthy volunteer group:
- •Age 18 years or older.
- •No known epilepsy or history of seizures.
- •Provision of written informed consent.
排除标准
- •Epilepsy group:
- •A neurodevelopmental disorder that prevents cooperation or following instructions.
- •A physical condition that prevents placement of the chest band.
- •Inability to securely attach the electrocardiography (ECG) or EEG sensors for reliable recording.
- •Absence of an epilepsy diagnosis or identification of psychogenic nonepileptic seizures.
- •Pregnancy or breastfeeding.
- •An implanted cardiac or neurostimulation device, such as a pacemaker, vagus nerve stimulator (VNS), or deep brain stimulation (DBS) device.
- •Allergy, skin sensitivity, or a skin lesion that prevents use of the electrodes or chest band.
- •A concomitant disease or medication use that may substantially affect heart rate or heart rate variability (HRV).
- •Having been diagnosed with major depressive disorder.
- •Healthy volunteer group:
- •A history of epilepsy or seizures.
- •Cardiovascular disease, a condition that may cause autonomic neuropathy, or medication use that may substantially affect heart rate or HRV.
- •Allergy, skin sensitivity, or a skin lesion that prevents use of the electrodes or chest band.
- •Having been diagnosed with major depressive disorder.
- •Pregnancy or breastfeeding.
- •Analysis-specific provisions:
- •The absence of a recorded target seizure during monitoring will not by itself exclude an epilepsy participant from the study; these recordings may contribute to heart rate and HRV analyses. Recordings from participants with one or two target seizures may also contribute to patient-independent machine learning models. Patient-specific machine learning models require at least three recorded target seizures from the same participant.
研究组 & 干预措施
Drug-resistant temporal lobe epilepsy
20 adults aged 18 years or older with drug-resistant temporal lobe epilepsy undergoing clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Participants have ongoing seizures despite adequate trials of at least two appropriately selected and tolerated antiseizure medicines, and a history or clinical suspicion of focal to bilateral tonic-clonic seizures. Textile-based wearable electrocardiography (ECG) will be recorded simultaneously with routine video-EEG. Recording includes a one-hour awake resting period. Routine monitoring duration and clinical care will not be changed for the study.
干预措施: Textile-based wearable electrocardiography (Device)
Healthy volunteers
20 healthy adults aged 18 years or older without known epilepsy or a history of seizures. Volunteers will be selected to achieve age and sex distributions as similar as feasible to the epilepsy group. Participants will undergo 24 hours of simultaneous video-electroencephalography (video-EEG) and textile-based wearable electrocardiography (ECG) at the same hospital as the epilepsy group, using a similar recording setup. Recording includes a one-hour awake resting period. Heart rate and heart rate variability will be compared with the epilepsy group.
干预措施: Textile-based wearable electrocardiography (Device)
结局指标
主要结局
Mean heart rate by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Mean heart rate will be calculated in beats per minute for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SDNN by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The standard deviation of normal-to-normal heartbeat intervals (SDNN) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: RMSSD by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The root mean square of successive differences between normal-to-normal heartbeat intervals (RMSSD) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: pNN50 by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
pNN50 is the percentage of successive normal-to-normal heartbeat interval pairs that differ by more than 50 milliseconds. It will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: low-frequency power by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Low-frequency (LF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: high-frequency power by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
High-frequency (HF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: LF/HF ratio by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The ratio of low-frequency power to high-frequency power (LF/HF) of normal-to-normal heartbeat interval variability will be calculated for each analyzed recording period. The ratio is dimensionless. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SD1 by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Poincare plot SD1 is the standard deviation of normal-to-normal heartbeat interval pairs perpendicular to the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SD2 by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Poincare plot SD2 is the standard deviation of normal-to-normal heartbeat interval pairs along the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: sample entropy by recording state
时间窗: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Sample entropy, a dimensionless measure of the irregularity of normal-to-normal heartbeat interval sequences, will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Event-level sensitivity for focal to bilateral tonic-clonic seizure detection
时间窗: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The percentage of eligible focal to bilateral tonic-clonic seizures confirmed by video-EEG that are correctly detected by each model: correctly detected seizures divided by all eligible reference seizures in the test recordings, multiplied by 100. Reference events will be annotated independently of ECG changes and model outputs. Alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
False alarms per hour for seizure detection
时间窗: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The number of detection alarms not matched to a reference seizure, divided by the total evaluable monitoring time in hours. Reference seizures will be established by independent video-EEG annotation. Alarm-to-event matching and alarm-merging rules will be specified before outcome analysis and applied consistently to held-out recordings. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Event-level sensitivity for focal to bilateral tonic-clonic seizure prediction
时间窗: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The percentage of eligible lead focal to bilateral tonic-clonic seizures correctly preceded by a model warning within the prespecified prediction timing rules. Correctly predicted lead seizures will be divided by all eligible lead seizures in the test recordings and multiplied by 100. Independent targets must be preceded by at least two seizure-free hours. The prediction horizon, seizure occurrence period and alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
False alarms per hour for seizure prediction
时间窗: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The number of prediction alarms not followed by an eligible reference seizure within the prespecified seizure occurrence period, divided by the total evaluable monitoring time in hours. Lead seizure eligibility, prediction horizon, seizure occurrence period and alarm-matching rules will be specified before outcome analysis. Video-EEG annotations will provide the reference events. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
次要结局
- Window-level classification sensitivity(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level classification specificity(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level positive predictive value(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level negative predictive value(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level F1 score(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level balanced accuracy(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level receiver operating characteristic area under the curve(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level precision-recall area under the curve(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Window-level classification accuracy(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Mean seizure detection latency(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Mean seizure prediction lead time(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)
- Percentage of evaluable recording time spent in seizure warning(During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.)