Prospective Assessment of the Diagnostic Accuracy of Multi-site Photoplethysmography Pulse Measurements for Diagnosis of Peripheral Artery Disease in Primary Care.

Peripheral arterial disease (PAD) is associated with cerebral and coronary artery disease. Symptomatic PAD affects about 5% of people over 55 years; many more have asymptomatic PAD. Early detection enables modification of arterial disease risk factors. Diagnostically, assessment of symptoms or signs can be unreliable; ankle brachial pressure index (ABPI) testing is time-consuming and few healthcare professionals are properly trained. This study assessed the diagnostic accuracy of multi-site photoplethysmography (MPPG), an alternative non-invasive test for PAD, in primary care. PAD patients identified from general practice registers were age- and sex-matched with controls. Participants were assessed using MPPG, ABPI and duplex ultrasound (DUS). Outcome measures were sensitivity and specificity of MPPG and ABPI (relative to DUS) and concordance. MPPG test results were available in 249 of 298 eligible participants from 16 practices between May 2015 and November 2016. DUS detected PAD in 101/249 (40.6%). MPPG sensitivity was 79.8% (95% confidence interval [CI] 69.9-87.6%), with specificity 71.9% (95% CI 63.7-79.2%). ABPI sensitivity was 80.2% (95% CI 70.8-87.6%), with specificity 88.6% (95% CI 82-93.5%). With comparable sensitivity to ABPI, MPPG is quick, automated and simpler to do than ABPI; it offers the potential for rapid and accessible PAD assessments in primary care.

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.