  Blood pressure (BP) changes are linked to individual health status in both
clinical and non-clinical settings. This study developed a deep learning model
to classify systolic (SBP), diastolic (DBP), and mean (MBP) BP changes using
photoplethysmography (PPG) waveforms. Data from the Vital Signs Database
(VitalDB) comprising 1,005 ICU patients with synchronized PPG and BP recordings
was used. BP changes were categorized into three labels: Spike (increase above
a threshold), Stable (change within a plus or minus threshold), and Dip
(decrease below a threshold). Four time-series classification models were
studied: multi-layer perceptron, convolutional neural network, residual
network, and Encoder. A subset of 500 patients was randomly selected for
training and validation, ensuring a uniform distribution across BP change
labels. Two test datasets were compiled: Test-I (n=500) with a uniform
distribution selection process, and Test-II (n=5) without. The study also
explored the impact of including second-deviation PPG (sdPPG) waveforms as
additional input information. The Encoder model with a Softmax weighting
process using both PPG and sdPPG waveforms achieved the highest detection
accuracy--exceeding 71.3% and 85.4% in Test-I and Test-II, respectively, with
thresholds of 30 mmHg for SBP, 15 mmHg for DBP, and 20 mmHg for MBP.
Corresponding F1-scores were over 71.8% and 88.5%. These findings confirm that
PPG waveforms are effective for real-time monitoring of BP changes in ICU
settings and suggest potential for broader applications.
