Continuous non-invasive blood pressure (BP) monitoring is a critical advancement in healthcare,
allowing for the early detection and management of hypertension one of the leading risk factors
for cardiovascular diseases [1, 2, 3]. Traditional cuff-based methods, though widely used, provide
only intermittent readings and fail to capture BP fluctuations throughout the day. To address
this limitation, machine learning models leveraging radar-based skin displacement signals have
been proposed for continuous BP estimation. However, the influence of demographic factors
such as age, gender, height, and weight on prediction accuracy remains insufficiently explored.
Existing studies primarily focus on machine learning models trained without explicit demographic
considerations. While some methods integrate features such as Pulse Transit Time
(PTT) from Photoplethysmography (PPG) sensors [4, 5, 6, 7], fewer approaches investigate the
role of demographic characteristics in radar-based BP monitoring [8]. This research aims to systematically
analyze the impact of demographic features on BP prediction using a Transformer-based
deep learning model.
The current methodology relies on pretraining a model on a large-scale arterial blood pressure
dataset (PulseDB [9]) and fine-tuning it with radar-based BP measurements [3] from human
participants. An ablation study is conducted to assess the contribution of individual demographic
features to model performance. Evaluation metrics include the Mean Absolute Error
(MAE) and Standard Deviation (STD), as well as compliance with established medical standards
such as those from the Association for the Advancement of Medical Instrumentation
(AAMI) [10] and the British Hypertension Society (BHS) [11].
The main objectives of this thesis include:
- Analyze the impact of demographic characteristics on BP prediction accuracy.
- Compare the performance of two distinct model architectures: a feed-forward neural
network and a transformer network.
To achieve these objectives, the following proposed steps will be undertaken:
- Analyze and Compare the distribution of the demographic features in both datasets
- retrain both model architectures on a large-scale arterial blood pressure dataset
- Finetune both model architectures on the radar dataset
- Evaluate and assess the model’s performance against set criteria.
References
[1] X. Xing, Z. Ma, M. Zhang, Y. Zhou, W. Dong, and M. Song, “An Unobtrusive and
Calibration-free Blood Pressure Estimation Method using Photoplethysmography and Biometrics,”
vol. 9, no. 1, p. 8611.
[2] D. Barvik, M. Cerny, M. Penhaker, and N. Noury, “Noninvasive Continuous Blood Pressure
Estimation From Pulse Transit Time: A Review of the Calibration Models,” vol. 15,
pp. 138–151.
[3] N. Vysotskaya, C. Will, L. Servadei, N. Maul, C. Mandl, M. Nau, J. Harnisch, and A. Maier,
“Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility
Study,” vol. 23, no. 8, p. 4111.
[4] S. González, W.-T. Hsieh, and T. P.-C. Chen, “A benchmark for machine-learning based
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[5] R. Mukkamala, J.-O. Hahn, O. T. Inan, L. K. Mestha, C.-S. Kim, H. Töreyin, and S. Kyal,
“Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice,”
vol. 62, no. 8, pp. 1879–1901.
[6] S. Maqsood, S. Xu, M. Springer, and R. Mohawesh, “A Benchmark Study of Machine
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[7] C. El-Hajj and P. Kyriacou, “A review of machine learning techniques in photoplethysmography
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[8] N. Vysotskaya, N. Maul, A. Fusco, S. Hazra, J. Harnisch, T. Arias-Vergara, and A. Maier,
“Transforming Cardiovascular Health: A Transformer-Based Approach to Continuous,
Non-Invasive Blood Pressure Estimation via Radar Sensing,” in ICASSP 2024 – 2024
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
pp. 2041–2045.
[9] W. Wang, P. Mohseni, K. L. Kilgore, and L. Najafizadeh, “PulseDB: A large, cleaned
dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation
methods,” vol. 4, p. 1090854.
[10] G. S. Stergiou, B. Alpert, S. Mieke, R. Asmar, N. Atkins, S. Eckert, G. Frick, B. Friedman,
T. Graßl, T. Ichikawa, J. P. Ioannidis, P. Lacy, R. McManus, A. Murray, M. Myers,
P. Palatini, G. Parati, D. Quinn, J. Sarkis, A. Shennan, T. Usuda, J. Wang, C. O. Wu, and
E. O’Brien, “A Universal Standard for the Validation of Blood Pressure Measuring Devices:
Association for the Advancement of Medical Instrumentation/European Society of Hypertension/
International Organization for Standardization (AAMI/ESH/ISO) Collaboration
Statement,” vol. 71, no. 3, pp. 368–374.
[11] E. O’Brien, “Blood pressure measuring devices: Recommendations of the European Society
of Hypertension,” vol. 322, no. 7285, pp. 531–536.