(271 days)
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.
| Characteristic | Acceptance Criteria (from Predicate) | Reported Device Performance (Subject Device) |
|---|---|---|
| Seizure Detection Algorithm Performance (GTC Seizures) | Detects GTC seizure patterns associated with unilateral, appendicular, tonic extension. | Algorithm is identical to the predicate. |
| Alarm Latency | 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. | 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. (Explicitly stated in Limitations, implying maintained performance). |
| sEMG Sampling Rate | 1,000 Hz | 1,000 Hz |
| sEMG Frequency Bands of Interest | 30-40 Hz, 130-240 Hz, and 300-400 Hz | 30-40 Hz, 130-240 Hz, and 300-400 Hz |
| Default Alarm Threshold | 135 | 135 |
| Physical Dimensions, Mass, Controls | H=3.44", W=2.34", D=1.33"; 127g; Power On/Off, Alarm, Cancel Buttons | H=3.44", W=2.34", D=1.33"; 127g; Power On/Off, Alarm, Cancel Buttons |
| Biocompatibility | Meets ISO 10993 standards (Parts 1, 5, 10). | Electrode patch underwent biocompatibility testing per ISO 10993 (Parts 1, 5, 10) to validate new electrode. |
| Electrical Safety & EMC | Meets IEC 60601-1:2005 (3rd Ed.), IEC 60601-1-2:2014 (4th Ed.), IEC 60601-1-8 (as applicable). | Verification conducted against these standards. |
| Usability | Meets IEC 60601-1-6. | Usability testing performed. |
| Home Healthcare Environment | Meets IEC 60601-1-11:2010 (1st Ed.). | Version no longer FDA recognized, but design changes are minor and outside the scope of this test (e.g., electrode patch testing). Implied continued compliance due to minor changes. |
| Electrode Adherence/Comfort | Adequate contact with patient's arm (implied by predicate function). | New electrode patch increased in surface area to improve comfort while maintaining integrity; electrode testing performed to validate. |
| Software Functionality (Record Only Mode) | Recording of sEMG data. | New feature to disable alarms for "Record Only Mode" while continuing to record sEMG data. Verified to maintain intended use. |
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.
§ 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.