MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure.

OBJECTIVE: To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings. METHODS: A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV). RESULTS: Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively. CONCLUSION: The findings suggest high potential for SPG-based BP estimation at the fingertip. SIGNIFICANCE: This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.

Fingertip Strain Plethysmography: Representation of Pulse Information based on Vascular Vibration.

This study presents fingertip strain plethysmography (SPG) as a visual trace of cardiac cycles in peripheral vessels. The setup includes a small, sensitive MEMS strain sensor attached to the fingertip to capture the pulsatile vibrations corresponding to cardiac cycles. SPG is evaluated on 10 healthy subjects for the estimation of heart rate (HR) and heart rate variability (HRV), as well as heartbeat-derived respiratory rate (RR) which is an HRV parameter. The estimated parameters are compared with a simultaneously-recorded electrocardiogram (ECG) for HR and HRV, and an inertial sensor placed on the chest wall for RR. Bland-Altman analyses suggest small estimation biases of 0.03 beats-per-minute (BPM) and 0.38 ms for HR and HRV respectively, demonstrating excellent agreement between fingertip SPG and ECG. The average estimation accuracies of 99.88% (± 0.04), 96.43% (± 1.44), and 92.64% (± 2.30) for HR, HRV, and RR respectively, prove the reliability of SPG for hemodynamic monitoring.Clinical Relevance- Conventional plethysmography sensors are either cumbersome or susceptible to skin color. This effort is a fundamental step towards the augmentation of conventional methods, thus ensuring stable, clinical-grade hemodynamic monitoring.

A Synthetic Ultra-Wideband Transceiver for Millimeter-Wave Imaging Applications.

In this work, we present a transceiver front-end in SiGe BiCMOS technology that can provide an ultra-wide bandwidth of 100 GHz at millimeter-wave frequencies. The front-end utilizes an innovative arrangement to efficiently distribute broadband-generated pulses and coherently combine received pulses with minimal loss. This leads to the realization of a fully integrated ultra-high-resolution imaging chip for biomedical applications. We realized an ultra-wide imaging band-width of 100 GHz via the integration of two adjacent disjointed frequency sub-bands of 10-50 GHz and 50-110 GHz. The transceiver front-end is capable of both transmit (TX) and receive (RX) operations. This is a crucial component for a system that can be expanded by repeating a single unit cell in both the horizontal and vertical directions. The imaging elements were designed and fabricated in Global Foundry 130-nm SiGe 8XP process technology.

Deep learning for tumor margin identification in electromagnetic imaging.

In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or during the excision surgery. To this end, the border between the normal and tumor parts needs to be identified. Therefore, the images need to be segmented into tumor and normal areas. We propose a deep learning technique which divides the electromagnetic images into two regions: tumor and normal, with high accuracy. We formulate deep learning from a perspective relevant to electromagnetic image reconstruction. A recurrent auto-encoder network architecture (termed here DeepTMI) is presented. The effectiveness of the algorithm is demonstrated by segmenting the reconstructed images of an experimental tissue-mimicking phantom. The structure similarity measure (SSIM) and mean-square-error (MSE) average of normalized reconstructed results by the DeepTMI method are about 0.94 and 0.04 respectively, while that average obtained from the conventional backpropagation (BP) method can hardly overcome 0.35 and 0.41 respectively.

Diagnosis of Coexisting Valvular Heart Diseases Using Image-to-Sequence Translation of Contact Microphone Recordings.

OBJECTIVE: Development of a contact microphone-driven screening framework for the diagnosis of coexisting valvular heart diseases (VHDs). METHODS: A sensitive accelerometer contact microphone (ACM) is employed to capture heart-induced acoustic components on the chest wall. Inspired by the human auditory system, ACM recordings are initially transformed into Mel-frequency cepstral coefficients (MFCCs) and their first and second derivatives, resulting in 3-channel images. An image-to-sequence translation network based on the convolution-meets-transformer (CMT) architecture is then applied to each image to find local and global dependencies in images, and predict a 5-digit binary sequence, where each digit corresponds to the presence of a specific type of VHD. The performance of the proposed framework is evaluated on 58 VHD patients and 52 healthy individuals using a 10-fold leave-subject-out cross-validation (10-LSOCV) approach. RESULTS: Statistical analyses suggest an average sensitivity, specificity, accuracy, positive predictive value, and F1 score of 93.28%, 98.07%, 96.87%, 92.97%, and 92.4% respectively, for the detection of coexisting VHDs. Furthermore, areas under the curve (AUC) of 0.99 and 0.98 are respectively reported for the validation and test sets. CONCLUSION: The high performances achieved prove that local and global features of ACM recordings effectively characterize heart murmurs associated with valvular abnormalities. SIGNIFICANCE: Limited access of primary care physicians to echocardiography machines has resulted in a low sensitivity of 44% when using a stethoscope for the identification of heart murmurs. The proposed framework provides accurate decision-making on the presence of VHDs, thus reducing the number of undetected VHD patients in primary care settings.