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The SmartMonitor® 2 Infant Apnea Monitor is intended for use in the continuous monitoring of respiration and heart rate of infant patients in a home or hospital environment. The monitor detects and alarms for periods of central apnea and high or low heart rates.

The SmartMonitor 2 is a monitoring device designed to monitor respiration and heart rate. Upon detection of abnormal events, SmartMonitor 2 alerts the caregiver via both visual and audible alarms and records the information for subsequent clinical review.

SmartMonitor 2 acquires the electrical activity of the heart via a two or three-lead electrode configuration. The same set of electrodes is used to measure transthoracic impedance and to subsequently develop a respiration signal. Detection of heart beats and respiration breaths is accomplished via softwarebased algorithms, which analyze the ECG and respiration signals. When beats or breaths are detected, SmartMonitor 2 provides feedback by blinking the Heart and Respiration LED's and calculates apnea intervals, average heart rates, and average breath rates for the purpose for identifying ECG and respiration rates that violate preset threshold values. In addition to the alarms, when abnormal ECG and respiration rates are detected, both tabular data and associated waveforms are logged in nonvolatile memory for subsequent review by a Health Care Professional.

Here's an analysis of the provided text regarding the Respironics SmartMonitor 2, broken down by your requested categories:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides performance specifications for both Respiration and ECG monitoring. These specifications serve as the acceptance criteria. The document states that the SmartMonitor 2 "meets the requirements specified in the Product Specification and that it conforms to the required standards," indicating it achieved these criteria.

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

The document states: "Data were compiled from multiple sites on at least 25 patients."

• Sample size for test set: At least 25 patients.
• Data Provenance: Not explicitly stated (e.g., country of origin, specific demographics). The study was a comparison of the SmartMonitor 2 to a predicate, and the data was "compiled from multiple sites," implying it was real-world clinical data. It's likely retrospective as it involves comparing to "predicate devices" and using "hand scoring" of data, suggesting analysis of pre-existing records rather than a forward-looking, interventional study. However, the document doesn't explicitly state retrospective or prospective.

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

The document indicates that the "gold standard" for ground truth was "hand scoring."

• Number of experts: Not specified.
• Qualifications of experts: Not specified (e.g., radiologist with 10 years of experience). It simply refers to "hand scoring," implying trained personnel who interpret the raw physiological signals.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (like 2+1 or 3+1). It only mentions "hand scoring" as the "gold standard," which could imply a single scorer or an internal process not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No MRMC study is mentioned. The clinical study described compares the device (SmartMonitor 2) against a "gold standard" (hand scoring) and aims to show substantial equivalence to a predicate device, not to evaluate human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance evaluation was done. The clinical protocol compared the SmartMonitor 2's performance (its algorithms for detecting apnea) "to the gold standard of hand scoring." This directly assesses the algorithm's performance without a human in the loop for the detection task itself, though a healthcare professional reviews the logged data. The device's primary function is automated detection and alarm.

7. The Type of Ground Truth Used

The type of ground truth used for the clinical study was expert consensus (implied via "hand scoring") on the physiological signals (respiration and heart rate) to identify central apnea events.

8. The Sample Size for the Training Set

The document does not specify a separate training set. The descriptions of "system qualification testing" and "software testing" refer to verification and validation activities. The clinical study described is for validation of the final device, not for training a model. Given the time period (2002) and the nature of apnea monitors, it's highly likely it was based on deterministic algorithms rather than machine learning models requiring extensive training data.

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

As no specific training set for a machine learning model is mentioned, the method for establishing ground truth for a training set is not applicable or provided. The device's performance is validated against clinical data after its algorithms are developed, implying ground truth for algorithm development would be based on physiological principles and expert-defined thresholds, not a machine learning training set in the modern sense.

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The HANNAH Wireless Vital Signs Monitor is indicated for the continuous monitoring of an infant's heart rate, respiration rate, and occurrences of central apnea in home, hospital and other environments.

The HANNAH system is an infant apnea monitor, which is substantially equivalent to legally marketed devices. The HANNAH system includes features incorporated in legally marketed devices. Like most legally marketed infant apnea monitors, the HANNAH incorporates sensors and alarms for monitoring both breath and heart rate. Like most legally marketed infant apnea monitors, the HANNAH system monitors heart rate using a 3-lead ECG measurement. The HANNAH system has the same technological characteristics as legally marketed devices. Because lead wires connected to the infant present a documented risk for strangulation and electrocution, and because lead wires are the source of a substantial majority of the false alarms associated with wired monitors, the decision was made to use radio frequencies to transmit the monitoring information from infant-placed sensors to the central unit. Further, since the more common method of monitoring, impedance pneumography, is highly susceptible to false readings due to interference with cardiac signals and motion artifacts, respiration is monitored using a pressure sensor instead.

The provided text describes the 510(k) summary for the HANNAH Wireless Vital Signs Monitor, which is an infant apnea monitor. It explicitly states that a clinical study was conducted for "clinical performance relative to a legally marketed predicate device." However, the document does not contain specific acceptance criteria, reported device performance metrics against those criteria, or the detailed methodology of the study.

Therefore, for the requested information, much of it is not available in the provided text.

Here's an overview of what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: Not specified in the provided text.
• Reported Device Performance: Not specified in the provided text. The text only mentions "a clinical study of the device was conducted by an independent contract research organization in order to evaluate the HANNAH monitor's clinical performance relative to a legally marketed predicate device." No specific performance metrics or comparative results are given.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not specified in the provided text.
• Data Provenance: Not specified in the provided text (country of origin, retrospective/prospective). The study was conducted by "an independent contract research organization."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not specified in the provided text. The nature of "ground truth" for an infant apnea monitor would likely involve direct observation or a highly accurate reference device, but the specifics are not detailed.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not specified in the provided text.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• The device is an infant apnea monitor, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study with human readers would not be applicable in this context. The study mentioned was to evaluate the monitor's clinical performance relative to a predicate device, implying a comparison of device performance itself, not human interpretation improvement.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• The described device is a "monitor," suggesting it operates as a standalone system to detect vital signs and apnea. The clinical study evaluated the HANNAH monitor's performance "relative to a legally marketed predicate device," which typically implies testing the device's output (measurements, alarms) against a reference. This aligns with a standalone performance evaluation of the device's sensing and processing capabilities. However, specific performance metrics or details of the standalone evaluation are not provided.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Not explicitly stated. For a vital signs monitor, ground truth would typically be established by a gold-standard measurement device or direct clinical observation by healthcare professionals.

8. The sample size for the training set

• Not applicable as this is a medical device (monitor) primarily relying on sensor technology and signal processing, not a machine learning model that requires a distinct "training set" in the conventional sense.

9. How the ground truth for the training set was established

• Not applicable for the same reason as above.

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