Sleep Profiler PSG2TM
The Sleep Profiler PSG2TM combines the accuracy and versatility of the Sleep ProfilerTM with the cardio-respiratory signals needed for a Type 2 unattended, virtually wireless polysomnography study.
The 13-channel system includes EEG, EOG and EMG, wireless oximetry, nasal pressure/airflow, chest and abdomen respiratory effort (RIP or piezo), forehead and finger pulse rate, head movement and position, and quantitative snoring.
The Sleep Profiler PSG2TM is the only home sleep apnea test (HSAT) that uses voice messages to assist the patient to correctly self-apply the sensors. Real-time detection of poor airflow and oximetry signal quality trigger corrective voice alerts that contribute to an industry-leading 94% success rate for a Type II study.
The web-based software enables selection of either 3% or 4% desaturation criteria, with auto-scored apnea/hypopnea indexes based on both AASM²⁰¹² and AASM²⁰⁰⁷ scoring rules and automated detection of poor signal quality.
Sleep-Disordered Breathing
- Detection of apneas, hypopnea-desats, and hypopnea-arousals
- Differentiation between obstructive and central apneas
- Hypoxemic exposure during sleep
- REM and positional influence on OSA severity
- Frequency and intensity of positional snoring
- Power spectral characteristics of the sleep stages
FEATURES
- Records EEG, EOG, EMG, with optional submental EMG or ECG, pulse rate, head position, head movement, quantitative snoring
- Auto-staging accuracy equivalent to PSG-based manual staging variability with:
- sleep spindles, spindle duration and power spectral densities
- movement arousals, cortical arousals, microarousals and autonomic activations
- sleep latency, REM latency, N3 latency
- Monitors study status and automatically sends reminder emails
- Multi-night studies with night-to-night comparisons
- Up to 30 hours of in-home recording between battery charges
- Objective vs. subjective comparison report
- Continuous 24/7 monitoring available for the ICU or Hospital
- Available with application of novel sleep biomarkers