Using speech data, researchers aimed to discover early indications of Parkinson’s disease. The researchers employed artificial intelligence (AI) to analyse and interpret voice signals in their study, where computations and diagnoses are accomplished in seconds rather than hours.
Rytis Maskeliunas, a Lithuanian researcher from Kaunas University of Technology (KTU), and colleagues from the Lithuanian University of Health Sciences (LSMU) attempted to identify early indications of Parkinson’s disease using speech data. Parkinson’s disease is often characterised by a loss of motor function, such as hand tremors, muscular rigidity, or balance issues.
According to Maskeliunas, a researcher at KTU’s Department of Multimedia Engineering, as motor activity decreases, so does the function of the vocal cords, diaphragm, and lungs: “Changes in speech often occur even earlier than motor function disorders, which is why the altered speech might be the first sign of the disease.”
According to Professor Virgilijus Ulozas, at the Department of Ear, Nose, and Throat at the LSMU Faculty of Medicine, patients with early-stage of Parkinson’s disease, might speak in a quieter manner, which can also be monotonous, less expressive, slower, and more fragmented, and this is very difficult to notice by ear. As the disease progresses, hoarseness, stuttering, slurred pronunciation of words, and loss of pauses between words can become more apparent.
Taking these symptoms into account, a joint team of Lithuanian researchers has developed a system to detect the disease earlier.
“We are not creating a substitute for a routine examination of the patient — our method is designed to facilitate early diagnosis of the disease and to track the effectiveness of treatment,” says KTU researcher Maskeliunas.
According to him, the link between Parkinson’s disease and speech abnormalities is not new to the world of digital signal analysis — it has been known and researched since the 1960s. However, as technology advances, it is becoming possible to extract more information from speech.
In their study, the researchers used artificial intelligence (AI) to analyse and assess speech signals, where calculations are done and diagnoses made in seconds rather than hours. This study is also unique — the results are tailored to the specifics of the Lithuanian language, in this way expanding the AI language database.
Speaking about the progress of the study, Kipras Pribuisis, lecturer at the Department of Ear, Nose, and Throat at the LSMU Faculty of Medicine, emphasises that it was only carried out on patients already diagnosed with Parkinson’s: “So far, our approach is able to distinguish Parkinson’s from healthy people using a speech sample. This algorithm is also more accurate than previously proposed.”
In a soundproof booth, a microphone was used to record the speech of healthy and Parkinson’s patients, and an artificial intelligence algorithm “learned” to perform signal processing by evaluating these recordings. The researchers highlight that the algorithm does not require powerful hardware and could be transferred to a mobile app in the future.
“Our results, which have already been published, have a very high scientific potential. Sure, there is still a long and challenging way to go before it can be applied in everyday clinical practice,” says Maskeliunas.
According to the researcher, the next steps will be to increase the number of patients in order to collect more data and to see whether the suggested algorithm is superior to alternative approaches used for early Parkinson’s diagnosis. Furthermore, it will be important to test the algorithm not just in laboratory-like surroundings, but also at the doctor’s office or the patient’s home.
