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Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.

Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.

Brainstem Auditory Evoked Potentials (BAEP) contain valuable information about the condition of the neural fibers associated with the auditory pathways. Extraction of this information is a difficult task due to contamination by on-going scalp EEG. This thesis reviews the current processing techniques and introduces adaptive noise cancellation (ANC) using systolic arrays as an alternative to existing technology. Q-R decomposition theory is reviewed and an explanation of the mechanics of systolic adaptive noise cancellation (SANC) is presented. A modified Given's rotation algorithm is derived resulting in a saving of up to 2/3 in memory requirements. Real data were collected in the laboratory. Real and simulated data were processed to determine the characteristics and effectiveness of adaptive noise cancellation strategies. Successful ANC of BAEP was performed on simulated data using a number or signal-to-noise ratios (S/N), data sequence lengths, reference signals and filter parameter values. We conclude that systolic arrays are a very powerful and appropriate technique for the extraction or BAEPs. Correlation studies indicated that the pre-stimulus EEG signal is inadequately correlated to the primary signal for successful ANC or BAEP in real data. A multi-channel collection scheme is outlined for future collection or Evoked Potential data. A summary or experimental results is presented to address the problem or data collection and signal processing optimization. / Thesis / Master of Engineering (MEngr)

A wireless, battery operated pulse oximeter system with a forehead mounted optical sensor was designed in our laboratory. This wireless pulse oximeter (WPO) would enable field medics to monitor arterial oxygen saturation (SpO2) and heart rate (HR) information accurately following injuries, thereby help to prioritize life saving medical interventions when resources are limited. Pulse oximeters developed for field-based applications must be resistant to motion artifacts since motion artifacts degrade the signal quality of the photoplethysmographic (PPG) signals from which measurements are derived. This study was undertaken to investigate if accelerometer-based adaptive noise cancellation (ANC) can be used to reduce SpO2 and HR errors induced by motion artifacts typically encountered during field applications. Preliminary studies conducted offline showed that ANC can minimize SpO2 and HR errors during jogging, running, and staircase climbing. An 8th order LMS filter with ì = 0.01 was successfully implemented in the WPO's embedded microcontroller. After real-time adaptive filtering of motion corrupted PPG signals, errors for HR values ranging between 60 - 180BPM were reduced from 12BPM to 6BPM. Similarly, ambient breathing SpO2 errors were reduced from 5% to 2%.

"A wireless wearable battery-operated pulse oximeter has been developed in our laboratory for field triage applications. The wearable pulse oximeter, which uses a forehead-mounted sensor to provide arterial oxygen saturation (SpO2) and heart rate (HR) information, would enable field medics to monitor vital physiological information following critical injuries, thereby helping to prioritize life saving medical interventions. This study was undertaken to investigate if accelerometry (ACC)-based adaptive noise cancellation (ANC) is effective in minimizing SpO2 and HR errors induced during jogging to simulate certain motion artifacts expected to occur in the field. Preliminary tests confirmed that processing the motion corrupted photoplethysmographic (PPG) signals by simple Least-Mean-Square (LMS) and Recursive Least-Squares (RLS) ANC algorithms can help to improve the signal-to-noise ratio of motion-corrupted PPG signals, thereby reducing SpO2 and HR errors during jogging. The study showed also that the degree of improvement depends on filter order. In addition, we found that it would be more feasible to implement an LMS adaptive filter within an embedded microcontroller environment since the LMS algorithm requires significantly less operations."

Η παρούσα διπλωματική εργασία αποτελεί βιβλιογραφική έρευνα στο επιστημονικό πεδίο της προσαρμοστικής ακύρωσης θορύβου (ANC). Αρχικά γίνεται μια αναφορά στη γενική φιλοσοφία της ANC, παρατίθεται μια σύντομη ιστορική αναδρομή και αναφέρονται τα πεδία εφαρμογής της ANC. Γίνεται ανάλυση των βασικών κατηγοριών προσαρμοστικής ακύρωσης θορύβου βασισμένες στον εμπροσθοτροφοδοτούμενο και στον ανατροφοδοτούμενο έλεγχο. Πρώτα συζητείται η δομή του ευρυζωνικού προσαρμοστικού εμπροσθοτροφοδοτούμενου ελέγχου, με την εξαγωγή και την ανάλυση του αλγόριθμου FXLMS. Στη συνέχεια αναλύονται τα στενής ζώνης εμπροσθοτροφοδοτούμενα συστήματα, εισάγοντας τη μέθοδο σύνθεσης κυματομορφής, τα προσαρμοστικά φίλτρα αποκοπής και την ANC πολλαπλών συχνοτήτων. Έπειτα αναπτύσσεται η έννοια της προσαρμοστικής ακύρωσης μέσω ανατροφοδοτούμενου έλεγχου από τη σκοπιά των σημάτων αναφοράς προσδίδοντας μια συσχέτιση με τα συστήματα εμπροσθοτροφοδότησης. Εν συνεχεία, η ανάλυση των μονοκαναλικών συστημάτων επεκτείνεται στα πολυκαναλικά συστήματα ANC. Παρουσιάζονται διάφορες online τεχνικές μοντελοποίησης δευτερεύουσας διαδρομής. Επιπρόσθετα, παρουσιάζονται διάφοροι ειδικοί ANC αλγόριθμοι όπως δικτυωτή ANC, ANC στο πεδίο της συχνότητας, ANC υποζώνης και ο αναδρομικός αλγόριθμος ελαχίστων τετραγώνων (RLS). Τέλος παρουσιάζονται αναλυτικά οι εφαρμογές της προσαρμοστικής ακύρωσης σε πρακτικό και πειραματικό επίπεδο, ενώ δίνεται μια ποσοτική συνεισφορά στη μείωση θορύβου. / This thesis is a literature research in the scientific field of adaptive noise cancellation (ANC). Originally becomes a reference to the general philosophy of the ANC, given a brief historical overview and a reference to the fields of application of ANC . An analysis of the main categories of adaptive noise cancellation based on feed-forward and feedback control. First a discussion is made, of the structure of broadband adaptive feed-forward control by extracting and analyzing the FXLMS algorithm. Then an analysis of narrowband feedforward systems is given, introducing the waveform synthesis method, adaptive notch filters and multiple frequency ANC. Then we develope the concept of adaptive cancellation via feedback control from the perspective of reference signals giving a correlation to feedforward systems. Subsequently, the analysis of single channel systems extends in multi-channel ANC. Various online secondary path modeling techniques are presented. Additionally, several special ANC algorithms are presented, such as lattice ANC, ANC in the frequency domain, subband ANC and the recursive least squares algorithm (RLS). Finally applications of adaptive cancellation are presented in detail, on practical and experimental level, given a qualitative contribution to noise reduction.

Two experiments were conducted to investigate Adaptive Noise Cancelling and Cepstrum echo removal post-processing techniques on acoustic data from a linear microphone array in an anechoic chamber. A point source speaker driven with white noise was used as the primary signal. The first experiment included a background speaker to provide interference noise at three different Signal-to-Noise Ratios to simulate noise propagating down a wind tunnel circuit. The second experiment contained only the primary source and the wedges were removed from the floor to simulate reflections found in a wind tunnel environment. The techniques were applicable to both signal microphone and array analysis. The Adaptive Noise Cancellation proved successful in its task of removing the background noise from the microphone signals at SNRs as low as -20 dB. The recovered signals were then used for array processing. A simulation reflection case was analyzed with the Cepstral technique. Accurate removal of the reflection effects was achieved in recovering both magnitude and phase of the direct signal. Experimental data resulted in Cepstral features that caused errors in phase accuracy. A simple phase correction procedure was proposed for this data, but in general it appears that the Cepstral technique is and would be not well suited for all experimental data. / Master of Science

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