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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.

2. Sample Size and Data Provenance

• 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

• 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.

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The SPEAC® System is indicated for use as an adjunct to seizure monitoring in adults in the home or healthcare facilities during periods of rest. The System records and stores surface electromyographic (sEMG) data for subsequent review by a trained healthcare professional.

The device is to be used on the belly of the biceps muscle to analyze sEMG signals that may be associated with generalized tonic-clonic (GTC) seizures. 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, formerly known as the Brain Sentinel® Monitoring and Alerting System (Predicate), is a physiological, surface electromyography (sEMG) monitor with or without alarms that records and stores data for review by a physician for characterization of seizure events. The System records sEMG data at 1,000 Hz and distributes physiological data. Data can be analyzed with an algorithm using the default threshold or by a modified threshold ordered by the physician. The sEMG monitor is worn unilaterally on the belly of the patient's biceps and it analyzes for sEMG GTC seizures and provide local, remote, audible, and visual seizure alarms when a GTC Seizure pattern that may be associated with such seizures that are detected. The SPEAC System provides sEMG recordings and audio data to physicians (or other trained healthcare professionals) for post-hoc review so that they may quantify and qualify the types of seizure events that their patients experience. Every 24 hours, the sEMG monitor is removed from the patient and replaced with the second sEMG Monitor on the opposite arm of the patient. The sEMG that is removed after 24-hours is then attached to a Base Station. By connecting the sEMG Monitor to the Base Station, the monitor charges and the recorded data is downloaded to the Base Station. The recorded data is then automatically uploaded to Brain Sentinel's cloudbased storage unit, Data Distribution System (DDS), where they await review by a physician. All patient data is cyber-secured within Microsoft Azure which is FedRAMP certified.

Here's an analysis of the provided text to extract the acceptance criteria and study information:

Acceptance Criteria and Device Performance Study for the SPEAC® System

The information provided describes the Brain Sentinel, Inc. SPEAC® System, a non-EEG physiological signal-based seizure monitoring system. This 510(k) submission (K182180) emphasizes the substantial equivalence to its predicate device (DEN140033), also from Brain Sentinel.

The document focuses on demonstrating that the modified SPEAC® System remains substantially equivalent to the predicate device rather than presenting a new, comprehensive study with specific acceptance criteria directly tied to a new device performance study. Instead, it relies on the predicate's established performance and confirms that the changes to the subject device (SPEAC® System) do not negatively impact those established characteristics.

Therefore, the "acceptance criteria and reported device performance" as requested would primarily refer to the performance established for the predicate device, which is maintained by the subject device. The primary "study" that proves the device meets "acceptance criteria" here is a demonstration of substantial equivalence, relying heavily on the predicate's performance and verification that minor changes do not alter essential safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) for substantial equivalence and the provided document, the "acceptance criteria" are implied to be the established performance characteristics of the predicate device, which the subject device is shown to maintain. The reported device performance is largely a re-affirmation of the predicate's performance, as the core seizure detection algorithm is identical.

2. Sample Size Used for the Test Set and Data Provenance

The document explicitly states: "The sEMG based seizure detection algorithm is identical to the predicate." It does not provide new clinical data or a new test set for the algorithm's performance. The performance metrics cited (e.g., alarm latency) appear to be derived from the studies that supported the predicate device (DEN140033). Therefore:

• Sample Size for Test Set: Not specified in this document for the algorithm's core performance, as it relies on the predicate's established performance. The "Performance Validation of SPEAC System" would likely have involved technical validation rather than a new clinical test set for algorithm accuracy.
• Data Provenance: Not specified in this document for the original algorithm validation. Given the company is U.S.-based (San Antonio, Texas), it is likely the original predicate studies were conducted in the U.S. The studies for the predicate device would have been prospective to demonstrate its initial effectiveness. This submission (K182180) focuses on equivalence rather than new prospective clinical data for algorithm performance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not provided in the current document. Since the algorithm is identical to the predicate, any expert review and ground truth establishment would have occurred during the predicate's development and regulatory clearance process (DEN140033).

4. Adjudication Method

This information is not provided in the current document. As with ground truth establishment, this would have been part of the predicate device's validation.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

There is no indication of an MRMC comparative effectiveness study being performed for this 510(k) submission. The device is a "non-EEG physiological signal-based seizure monitoring system" with automated alarm capabilities, not a diagnostic imaging AI that assists human readers in interpretation. Its primary function is to detect specific sEMG patterns and alert caregivers, and to record data for subsequent review by a trained healthcare professional. The focus is on the device's ability to identify specific sEMG patterns, not on how it enhances human interpretation of complex data in a comparative setting.

6. Standalone (Algorithm Only) Performance Study

Yes, the algorithm only performance was established as part of the predicate clearance (DEN140033). The document states: "The sEMG based seizure detection algorithm is identical to the predicate." The performance claim for alarm latency ("The device provides an alert from -30.82 – 25.06 seconds, with an average of 5.34 seconds (SEM ± 2.86), following the onset of sEMG activity that may be associated with a GTC seizure.") directly reflects the standalone performance of this algorithm. This 510(k) reaffirms that this standalone performance is maintained.

7. Type of Ground Truth Used

The type of ground truth used for validating the sEMG patterns would typically be expert consensus or adjudicated clinical events, correlating the sEMG signals with observed generalized tonic-clonic (GTC) seizures. The document indicates that the system analyzes "sEMG signals that may be associated with generalized tonic-clonic (GTC) seizures" and identifies "sustained sEMG contraction patterns—during the tonic phase and early transition to the clonic phase—that are pathognomonic of GTC seizures." This strongly implies that the ground truth for detection was based on clinically confirmed GTC seizures, likely verified by neurologists or experts in epileptology, alongside simultaneous sEMG recordings.

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. This information would have been part of the original development and validation of the algorithm for the predicate device.

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. However, given the nature of the device and its indications, it is highly probable that the training data collected for the predicate device involved:

• Prospective collection of sEMG data from patients with confirmed epilepsy, particularly those prone to GTC seizures.
• Simultaneous video-EEG monitoring and direct clinical observation by trained medical staff to accurately identify and timestamp the onset and characteristics of GTC seizures.
• Annotation of sEMG recordings by experts (e.g., epileptologists, neurophysiologists) to delineate the specific sEMG patterns "pathognomonic of GTC seizures" as input for algorithm development.

This would involve a rigorous clinical process to ensure accurate correlation between the sEMG signals and the actual seizure events for both training and validation of the algorithm.

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