Abstract
During mechanical ventilation, a common problem known as patient-ventilator asynchrony (PVA) occurs when there is a mismatch between the needs of the patient’s breathing and the breath cycle delivered by the ventilator. PVA is problematic because it can be associated with adverse effects such as discomfort for the patient, increased work of breathing, longer mechanical ventilation duration and ventilator-induced lung injury. An automated means of early PVA detection and classification could lead to improved health outcomes and help reduce the impact of PVA on hospital resources. This paper presents a machine learning framework to detect PVA events using only the first half second of data after the start of a PVA event. When trained on more than 5000 PVA events sampled from 25 subjects, our logistic classifier achieves a sensitivity (specificity) of 99.81% (99.72%) for detecting PVA events. We then present a system capable of early classification of Ineffective Effort (IE) and Double Trigger (DT) events, which achieves a sensitivity (specificity) of 63.73% (92.88%). By demonstrating the feasibility of early PVA event detection and classification, our findings suggest that more effective intervention processes could be possible, including automated interventions with different response strategies for different PVA event types.
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Appendix - Description of Signals
Appendix - Description of Signals
Signal name | Description |
|---|---|
Oximetry | Blood oxygen levels, displayed as a percentage |
PtcCO2 | Transcutaneous carbon dioxide |
Leak - Tx | Air leakage from NIV mask |
Pos | Body position sensor: back-supine, front-prone, sides-left and right |
Light | Environmental light sensor (lights “on” or “off”) |
Pmask | NIV mask pressure signal |
Thor | Breathing effort detected by respiratory belt placed over thoracic region |
Abdo | Breathing effort detected by respiratory belt placed over abdominal region |
Flow - Tx | Airflow through nasal prongs |
DB Meter | Decibel meter to detect snoring |
ECG+ECG- | Cardiac activity and heart rate |
EMGs+- EMGs- | Electromyogram - electrodes placed on both sides of the jaw (bilateral masseter muscles) to detect clenching of the jaw (bruxism) during sleep |
E1 | Picks up eye muscle movement (E1 and E2). Electrode placed on outer corner of left eye |
E2 | Electrode placed on outer corner of right eye |
F4-M1 | Electrode placed on right side of head over the frontal area. Note: M1 refers to reference electrode placed over left mastoid bone area behind the left ear |
C4-M1 | Electrode placed on right side of the head, centrally |
O2-M1 | Electrode place on right side of the head, towards the lower back |
dEMG+-dEMG- | Electromyogram - electrodes placed over diaphragm area |
atEMG/L_T3-atEMG | Electromyogram - electrodes placed on left anterior tibialis muscle on the shin to detect periodic limb movement (PLM) during sleep |
atEMG/R_T4-atEMG | Electromyogram-electrodes placed on right anterior tibialis muscle on the shin to detect PLM during sleep |
Nasal pressure | Pressure signal derived from nasal prongs |
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Gao, E., Ristanoski, G., Aickelin, U., Berlowitz, D., Howard, M. (2022). Early Detection and Classification of Patient-Ventilator Asynchrony Using Machine Learning. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_23
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DOI: https://doi.org/10.1007/978-3-031-09342-5_23
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