(368 days)
The SPEAC® System is intended for use as an adjunct to seizure monitoring in adults in the home or healthcare facilities during periods of rest.
The non-EEG Physiological Signal Based Seizure Monitoring System continuously records and stores surface electromyographic (sEMG) data for subsequent review.
Trained healthcare professionals may use the electrophysiological sEMG data during a post-hoc review, with other contextual data, to characterize upper-extremity motor activity (UEMA) ipsilateral to the activity.
Audio data recorded during seizure monitoring may be available for review by a trained healthcare professional.
The device is to be used on the belly of the biceps muscle to analyze sEMG signals. When sEMG signal patterns associated with a unilateral, appendicular, tonic extension that could be associated with a GTC seizure are detected, the SPEAC System sends adjunctive alarms to alert caregivers.
Adjunctive alarms may be disabled by a physician order while continuing to record sEMG data for subsequent review.
The SPEAC System is a wireless, non-invasive, physiological, surface electromyography (sEMG) recording, monitoring, and alerting system to be used as an adjunct to seizure monitoring during periods of rest. The System continuously records and stores surface electromyographic (sEMG) data for subsequent review by a physician. Trained healthcare professionals may use the electrophysiological sEMG data, with other contextual data, to characterize seizures with upper-extremity motor activity ipsilateral to the device from other activity. SPEAC data gives healthcare professionals another diagnostic tool to characterize seizure events in a home or hospital setting.
The System continuously records and distributes sEMG data at 1,000 Hz (and audio around detected events) for post-hoc review by physicians (or other trained healthcare professionals) for the characterization of seizure events. A physician may perform post-hoc review of the SPEAC System data to characterize motor events that may be associated with seizures.
The seizure monitoring algorithm is able to send alarms to notify patients and caregivers when a pattern that may be associated with a generalized tonic-clonic (GTC) seizure is measured. Physicians may order the System with or without alarms and may order threshold adjustments to customize the level at which the System alarms.
Data collected by the System is uploaded to Brain Sentinel's secure remote storage, the Data Distribution System (DDS), and is remotely accessible for physician review. All patient data is cyber-secured within Microsoft Azure which is FedRAMP certified.
The SPEAC System remains the same with no alterations of any kind. The sEMG-based seizure monitoring algorithm is identical to the predicate SPEAC System. The purpose of this submission is to expand the indications for use based on clinical performance testing that was submitted to support a determination of substantial equivalence. When trained appropriately, clinicians may use the subject device to perform post-hoc analysis of the sEMG data from the device, with other contextual patient data, to characterize seizures with upper-extremity motor activity ipsilateral to the device from other activity.
The SPEAC System (K200276) is intended for use as an adjunct to seizure monitoring in adults. The system records and stores surface electromyographic (sEMG) data for subsequent review by trained healthcare professionals to characterize upper-extremity motor activity (UEMA) ipsilateral to the device. The system also sends alarms to alert caregivers when sEMG signal patterns associated with a unilateral, appendicular, tonic extension that could be associated with a Generalized Tonic-Clonic (GTC) seizure are detected.
Here's a breakdown of the acceptance criteria and the study details:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state formal "acceptance criteria" with specific numerical thresholds for the clinical study. Instead, the study evaluates the accuracy of expert reviewers in classifying seizure events using sEMG data. The reported device performance is presented as the accuracy of these expert reviewers.
| Performance Metric | Acceptance Criteria (Not Explicitly Stated as Numerical Thresholds) | Reported Device Performance (Accuracy from combined studies) |
|---|---|---|
| Overall Accuracy (individual assessments) | Implied: Ability of clinicians to characterize and differentiate types of seizure events with sEMG data | 83% (95% CI [0.78 0.88], n = 243) |
| Overall Accuracy (committee approach) | Implied: Ability of clinicians to characterize and differentiate types of seizure events with sEMG data | 86% (95% CI [0.77 0.93], n = 81) |
| Tonic-Clonic (TC) Seizures Accuracy (Committee) | Implied: High accuracy for GTC seizure characterization | 92% (95% CI [0.75 0.99]) |
| PNES (Psychogenic Non-Epileptic Seizures) Accuracy (Committee) | Implied: High accuracy for PNES characterization | 100% (95% CI [0.82 1.00]) |
2. Sample Size and Data Provenance
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Test Set Sample Size:
- Individual assessments: 243 events (n=243)
- Committee approach: 81 events (n=81)
- Specific breakdown by event type (for committee):
- Tonic-Clonic: 26
- Simple Motor ES (Tonic & Clonic): 14
- Complex Motor ES ("Other"): 22
- All ES (Epileptic Seizures): 62
- PNES, whole body involvement: 4
- PNES, arm jerks/hand tremors only: 15
- All PNES: 19
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Data Provenance: Prospective clinical trials conducted in an Epilepsy Monitoring Unit (EMU). The country of origin is not explicitly stated, but clinical trials subject to FDA review are typically conducted in the US or under internationally recognized standards.
3. Number of Experts and Qualifications
- Number of Experts: Three (3) sEMG reviewers participated in both studies.
- Qualifications of Experts: The document states "Physicians" for the sEMG review. For the ground truth, "Epileptologists" reviewed vEEG data, implying these are highly qualified medical professionals specializing in epilepsy. Specific years of experience are not mentioned for any of the reviewers.
4. Adjudication Method for the Test Set
- The study used a "committee" style approach, which is defined as majority rules (2/3) for combining the assessments of the three sEMG reviewers.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Yes, a form of MRMC study was done. The study involved multiple expert readers (three sEMG reviewers) evaluating multiple cases (seizure events).
- Effect Size of Human Readers with AI vs. without AI: This study's primary goal was not to measure the improvement of human readers with AI assistance. Instead, it evaluated the ability of clinicians to characterize events using sEMG data alone (from the device) and compared these characterizations to vEEG data and automated processing. The sEMG data from the SPEAC system is intended as an adjunct to monitoring, meaning human clinicians still interpret the data. Thus, an "AI vs. without AI assistance" effect size is not directly provided in the context of human reader improvement via an AI algorithm, but rather the utility of the sEMG data itself for expert interpretation.
6. Standalone (Algorithm Only) Performance
- Yes, a standalone component of performance was done. The clinical endpoints included comparison of the expert sEMG review to "automated event characterization". However, the results presented in the table specifically detail the "Expert Reviewer Accuracy" and indicate "No automated processing for seizure characterization was cleared in this 510(k)." This suggests that while automated processing was evaluated, its performance results are not provided here for clearance, and the focus for clearance is on the expert interpretation of the sEMG data.
7. Type of Ground Truth Used
- The ground truth for seizure characterization was established using Video-EEG (vEEG) data interpreted by epileptologists.
8. Sample Size for the Training Set
- The document does not specify a separate sample size for a training set. The study describes training physicians in interpreting sEMG data, but it refers to a prospective clinical trial where data was collected. It's unclear if a separate training data set for an algorithm was used, as the focus of the presented clinical performance is on human interpretation of sEMG data. The statement "The sEMG-based seizure monitoring algorithm is identical to the predicate SPEAC System" suggests the algorithm was already established, and this submission focused on expanding the indications for human interpretation of its output.
9. How Ground Truth for the Training Set Was Established
- If there was a training set for an algorithm, the document does not describe how its ground truth was established. For the training of physicians, the ground truth would implicitly be their consensus or established medical knowledge based on reference methods like vEEG, as discussed in the study.
§ 882.1580 Non-electroencephalogram (EEG) physiological signal based seizure monitoring system.
(a)
Identification. A non-electroencephalogram (non-EEG) physiological signal based seizure monitoring system is a noninvasive prescription device that collects physiological signals other than EEG to identify physiological signals that may be associated with a seizure.(b)
Classification. Class II (special controls). The special controls for this device are:(1) The technical parameters of the device, hardware and software, must be fully characterized and include the following information:
(i) Hardware specifications must be provided. Appropriate verification, validation, and hazard analysis must be performed.
(ii) Software, including any proprietary algorithm(s) used by the device to achieve its intended use, must be described in detail in the Software Requirements Specification (SRS) and Software Design Specification (SDS). Appropriate software verification, validation, and hazard analysis must be performed.
(2) The patient-contacting components of the device must be demonstrated to be biocompatible.
(3) The device must be designed and tested for electrical, thermal, and mechanical safety and electromagnetic compatibility (EMC).
(4) Clinical performance testing must demonstrate the ability of the device to function as an assessment aid for monitoring for seizure-related activity in the intended population and for the intended use setting. Performance measurements must include positive percent agreement and false alarm rate.
(5) Training must be provided for intended users that includes information regarding the proper use of the device and factors that may affect the collection of the physiologic data.
(6) The labeling must include health care professional labeling and patient-caregiver labeling. The health care professional and the patient-caregiver labeling must include the following information:
(i) A detailed summary of the clinical performance testing, including any adverse events and complications.
(ii) Any instructions technicians and clinicians should convey to patients and caregivers regarding the proper use of the device and factors that may affect the collection of the physiologic data.
(iii) Instructions to technicians and clinicians regarding how to set the device threshold to achieve the intended performance of the device.