IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.

Secondary Aural Symptoms in Relation to Cranio-Cervical and General Disorders

The present thesis comprises two study populations. The first study sample (SS1) consisted of 411 adults examined and interviewed at three annual visits. The second study sample (SS2) consisted of 1720 adults who filled in a mailed questionnaire about secondary otalgia, tinnitus and fullness of ears. In the second phase of the SS2, 100 subjects with otalgia were examined and interviewed by specialist in stomatognathic physiology and otorhinolaryngology. In the third phase, 36 subjects participated in a randomized, controlled and blinded trial of effectiveness of occlusal appliance on secondary otalgia, facial pain, headache and treatment need of temporomandibular disorders (TMD). The standardized prevalence of recurrent secondary otalgia was 6%, tinnitus 15% and fullness of ears 8%. Aural symptoms were more frequent among young than old subjects. They were associated with other, simultaneous aural symptoms, TMD pain, head and neck region pain, and visits to a physician. The subjects with aural symptoms more often had tenderness on palpation of masticatory muscles and clinical signs of temporomandibular joint than the subjects without. 85% of the subjects reporting secondary otalgia had cervical spine or temporomandibular disorder or both. In SS1, the final model of secondary otalgia included active need treatment for TMD, elevated level of stress symptoms, and bruxism. In SS2, the final models of aural symptoms included associated aural symptoms, young age, TMD pain, headache and shoulder ache. Stabilization splint more effectively alleviated secondary otalgia and active treatment need for TMD than a palatal control splint. In patients with aural pain, tinnitus or fullness of ears, it is important to first rule out otologic and nasopharyngeal diseases that may cause the symptoms. If no explanation for aural symptoms is found, temporomandibular and cervical spine disorders should be rouled out to minimize unnecessary visits to a physician.

Epidemiology, management and outcome of facial injuries

Dental injuries are common and the incidence of maxillofacial injuries has increased over the recent decades in Finland. Accidental injuries are the global leading cause of death among children over the age of one year and among adults under the age of 40 globally. Significant resources and costs are needed for the treatment of these patients. The prevention is the most economical way to reduce trauma rates and costs. For the prevention it is crucial to know the prevalences, incidences and risk factors related to injuries. To improve the quality of treatment, it is essential to explore the causes, trauma mechanisms and management of trauma. The above mentioned was the aim of this thesis. With a large epidemiological cohort study (5737 participants) it was possible to estimate lifetime prevalence of and risk factors for dental trauma in general population (Study I). The prevalence of dental fractures was 43% and the prevalence of dental luxations and avulsions was 14%. Male gender, a history of previous non-dental injuries, mental distress, overweight and high alcohol consumption were positively associated with the occurrence of dental injuries Study II was conducted to explore the differences in type and multiplicity of mandibular fractures in three different countries (Canada, Finland and Kuwait). This retrospective study showed that the differences in mandibular fracture multiplicity and location are based on different etiologies and demographic patterns. This data can be exploited for planning of measures to prevent traumatic facial fractures. The etiology, management and outcome of 63 pediatric skull base fracture (Study III) and 20 pediatric frontobasal fracture patients (Study IV) were explored. These retrospective studies showed that, both skull base fracture and frontobasa fracture are rare injuries in childhood and although intracranial injuries and morbidity are frequent, permanent neurological or neuropsychological deficits are infrequent. A systematic algorithm (Study V) for computer tomography (CT) image review was aimed at clinicians and radiologists to improve the assessment of patients with complex upper midface and cranial base trauma. The cohort study was cross sectional and data was collected in the Turku and Oulu University Hospitals. A novel image-reviewing algorithm was created to enhance the specificity of CT for the diagnosis of frontobasal fractures. The study showed that an image-viewing algorithm standardizes the frontobasal trauma detection procedure and leads to better control and assessment. The purpose of the retrospective subcranial craniotomy study (VI) was to review the types of frontobasal fractures and their management, complications and outcome when the fracture is approached subcranially. The subcranial approach appears to be successful and have a reasonably low complication rate. It may be recommended as the technique of choice in multiple and the most complicated frontal base fractures where the endoscopic endonasal approach is not feasible.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.