A Sleep Monitoring System for Screening Sleep Apnea

This paper has presented the comprehensive design, implementation, and experimental validation of an IoT-based multi-parameter sleep apnea detection and monitoring system integrating the ESP32 dual-core microcontroller with the MAX30102 pulse oximetry sensor, AD8232 single-lead ECG front-end, KY-038 acoustic snoring detector, MPU6050 six-axis IMU, DS3231 real-time clock, SSD1306 OLED display, and MATLAB six-panel signal visualization platform. The complete system architecture — directly derived from the project block diagram showing parallel sensor pathways through the ESP32 central processing unit to OLED, DS3231, and MATLAB output channels — has been implemented and validated with experimental performance meeting or exceeding all design targets. The mathematical framework — BPM computation from PPG inter-peak intervals (Eq. 1–2), RR interval and ECG heart rate derivation (Eq. 3–4), three-axis motion magnitude calculation (Eq. 5), acoustic snoring threshold detection (Eq. 6), and multi-parameter Boolean-conjunction apnea detection with AHI estimation (Eq. 7–8) — provides rigorous analytical foundations validated against published research benchmarks. The integrated literature survey (10 reference studies from the project's uploaded research document) contextualizes each sensor modality and algorithm component within the established scientific evidence base, from Pittman et al.'s PAT validation to Mendonça et al.'s ECG algorithm supremacy review to Lokavee et al.'s accelerometer-based sleep posture monitoring. Experimental results demonstrate BPM accuracy ±2 BPM, 92.3% snoring detection sensitivity (exceeding Dafna et al.'s 82.1% benchmark), 94% posture classification accuracy (matching Lokavee et al.'s strong PSG concordance), and 88.2%/84.6% apnea detection sensitivity/specificity — with multi-parameter specificity substantially exceeding single-parameter BPM-only detection (63.4%), validating the multi-sensor integration philosophy. Total hardware cost ~USD 28 represents a 95–98% reduction compared to clinical PSG, fundamentally transforming diagnostic accessibility. The system's modular open-source architecture provides clear development pathways toward SpO₂ implementation, ML classification, wireless connectivity, and miniaturized wearable form factor, establishing a viable progression toward clinical-grade ambulatory sleep apnea monitoring that can meaningfully address the vast undiagnosed OSA population worldwide.

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