additional 3 pages
Running head: BIOMEDICAL ACOUSTIC TECHNOLOGIES 1
BIOMEDICAL ACOUSTIC TECHNOLOGIES 21
Biomedical Acoustic Technologies
Name
Course
Instructor
Date
1. Introduction
The relationship between life sciences, electronics, and physical sciences has become very important to appoint where several researchers have focused on related studies to fulfill the desires of the medical scientist in biomedical society. Based on physical, biological, and chemical principles, the improvement of instruments burgeoned. The analytical instruments need various types of sensors beyond elementary instruments used in non-ionization radiation, ionization and flow and temperature measurements to ultrasound, chemical, biological, and acoustic sensing transducers. Real-time monitoring of biomedical signals is the main clue for executive management, prediction, earlier detection, and diagnosis of fatal diseases including heart attacks and strokes. Various biomedical signals can be obtained from various medical sensors, which need real-time analysis and processing for advancing healthcare and also the management of critical care situations. Different biomedical signals can be obtained to show the patient’s health status. The biomedical signals may include, voltage records, temperature records, Electrocardiograph to show the electrical activity of the heart, phonocardiogram, and electroencephalogram signals to show the brain’s electrical activity. Many parameters obtained are ineffective and pose a challenge to designers of electronics to deploy and process these signals. Although, there are defined characteristics associated with biomedical signals which remain constant even with the fluctuation in environmental conditions, which may include the equipment design, positioning of electrode, and availability of blood vessels or fats in locations the signals were obtained. Improvement in technology, sensors, and electronic fabrication/design provided various biomedical signals used for predicting, monitoring and detecting various diseases, concerning the obtained characteristics of biomedical signals, from the specified sensors, for even further processing of the signal, which is depicted as the energetic phase. Biomedical signals are produced from various sensors and they are connected to patients to obtain specific information related to a certain disease. In engineering, industrial, and medical applications, there is a rising demand for small, disposable, reliable, and inexpensive sensors (Wiklund et al., 2001). The market for sensors is rapidly rising. Especially, the market for biosensor is highly promising because of their application in healthcare, medical, and biotechnology applications, like cancer detection at early stages, pathogen detection, and glucose testing.
A sensor is a device that normally reacts to a specified input and detects or measures a certain property, condition, or records according to the information received. In general, any device designed to only that is condition-based or detects some property is just a detector and not a sensor (Zhang et al., 2011). Detectors have a crucial medicinal role, mostly in alarm systems. On the other hand, in biomedical sectors, a sensor is designed to respond to a specific physical input and recording the associated output. The biomedical and physical input may include any critical signs from patients and biochemical quantities. The sensors’ system comprise of electrical outputs that record the electrical signals. The signals are amplified then processed for final output to a recording chart, displaying monitor to a storage unit (Zhang et al., 2011). However, many sensors that are widely used do not show a linear performance. Sensors and transducers contain many synonymous terms; nevertheless, the transducers are commonly used for the transformation of sound energy into strain energy, and then another transducer is used in converting the same energy into an efficient and recordable electrical form of energy aimed to produce the entire sound sensor.
Biosensors can be defined as analytical devices that normally use a defined biological system when targeting molecules or macromolecules. Examples of biosensors include a physiochemical sensor aimed to transform the required biological signal from an undefined bio-recognition system to a defined, measured, and assessable signal. A biosensor comprises of three important modules which are the detector designed to detect the stimulus, transducer which converts stimulus to the desired output, and the system of output which amplifies and also displays the final output properly. Piezoelectricity phenomenon appears in crystals like quartz and Rochelle salt, where voltage convinces mechanical stress and the other way round. One of the most common sensors is the acoustic sensor which detects acoustic signals. Acoustic biosignals convey physiological data, ascending from the organ’s vital functions, and show the health state and the related cardiorespiratory pathologies (Shixi, 2006). Many acoustic biosignals are mainly initiated inside the body, like lung sounds, snoring sounds, and heart sounds, in regions where the vibrating structure in the human body normally produces acoustic sounds. Acoustic sounds are gradually inhibited during propagation through the skin tissues. The dissimilar body sounds interfere collectively causing mechanical vibrations of the skin and the vibrations are observed by the sensors and further converted to an assessable electrical signal. It is very informative to learn the sounds’ origin from an engineering and clinical point of view. Many extensive studies concerning sound physics and biology have been widely conducted.
A decade ago, it was predicted that the use of biomedical acoustic technologies would be very common. A couple of years before that, there were various advertisements made by more than one healthcare company claiming that these technologies would be used in many health facilities (Shixi, 2006). It was found that the amount and extent of long term installations were small and the reason was the high cost of biomedical acoustic technology installation which could entirely exceed the price of the control hardware by bigger amount. In 1997, a research clarified that what was effective had to be a cheaper and biomedical acoustic system which included sensor transducers to solve most challenges, and advanced software to help users in the perfect monitoring of patients’ health data. Many years since 1997, there was no production of such a system, although more work related to biomedical acoustic technologies continues in various laboratories in many places globally.
Acoustic technologies need to be in a digital design, where all the signals from the transducers, electromechanical or electroacoustic, are processed using DSP (digital signal processing) in real-time. In the 1980s, research showed the development of DSP chips which enabled the implementation of effective algorithms at a lowered cost and as a result, it facilitated more applications and developments of biomedical acoustic technologies (Shixi, 2006). The more the DSP is advanced, the more it is capable to handle the sophisticated algorithms and implement them in real-time resulting in improved performance of the healthcare sector. More sophisticated algorithms lead to faster and accurate convergence as well as efficiency in huge acoustic technologies and also more robust to any type of interference.
3. Objectives
Sensors are essential in many devices and various applications for a better future. The old health care systems has been facing a lot of challenges like unmonitored health which mostly led to unexpected calamities like death. This research expounds on the benefits and applications of biomedical acoustic technologies. It also creates a further understanding of these technologies and their implications in the modern era. The body of humans continuously communicates information concerning the people’s health that indicates the status of the organs and the general health information. This information can be captured using physical devices which measure various types of information like blood glucose, heart rate, brain capacity, nerve condition, among others. These measurements give detailed information that helps physicians in treatment and diagnosis decisions. Engineers are also realizing new and advanced mathematical formulas and algorithms. This paper also includes different classifications of biosignals basing arguments on various principles. Also, various biosensors are expounded including the function of the stage of bio-potential amplifier located within the sensor. Lastly, processing and acquisition phases in the biomedical signal are also included.
4. Data analysis and Discussion
All biomedical systems are known to generate signals to influence the body or even analyze bio-signals to obtain important information on the operation of the body. Signals can be generally useful as the checked parameters from an object; specifically, signals in biomedical analyses represent the psychological description of living objects (Sezen et al., 2007). In simple terms, the biomedical signal can be described as the signal that depicts biological information on the behavior or state of the specified living objects. The information acquired can be common/simple, like the wrist pulse and the blood pressure, or complex like the sophisticated information generated from analysis of the internal structure of soft tissues. Bio-signals are commonly used to determine the mechanism of a specified biological event or system.
The main types of biomedical signal include electroencephalogram and speech signals. There are five common sources of noise affecting bio-signals, including interference, sampling noise, aliasing, instrument noise, power line AC, and thermal noise (Sezen et al., 2007). Various classifications are used to categorize these bio-signals, according to channel numbers, biosignal source, nature, model, and dimensionality. Various practical problems arise when biomedical acoustic technologies are instilled in real applications. An essential strategy is used in healthcare performance analysis and it involves various techniques; it starts with a simplified problem then progressively summing constraints that are practical and different complexities. Performance analysis helps in resolving different challenges like; practical constraints that normally lower performance of a system, fundamental performance limitations, challenges in design architecture, performance balanced limitations.
At each different level, a certain degree of confidence is attained and a strong benchmark is created to compare and crosscheck the authenticity with the next stage of complexity. To improve efficiency for industrial application, the biomedical technology must contain some properties: autonomy concerning the installation, to ensure that the system could be created and preset at the place of manufacturing and inserted on-site; maximum efficiency in cases of high frequency and the capability to cancel a broad range of primary noise; advanced self-adaptability to ensure that the system can deal with different variations like pressure, temperature, etc.; and reliability and robustness of the various elements and simplifications of the electronics control. Sound is normally produced by vibrating elements (Sezen et al., 2007). It goes through a medium from two points. It is a wave generated because of the vibration of the sound through a non-vacuous medium through where the mechanical sound travels.
4.1 Acoustic Wave Technology
There are various techniques to measure sound waves like, frequency, amplitude which is based on the volume of the sound, wavelength, sound speed, and phase. Normally sound represents the motion of the wave with pressure difference due to the source of vibration and the particles moving only one tone of the sound (Wang et al., 2013). The discrete particles move about their point of relaxation at a similar frequency tone. The particles vibrating during motion pus the next ones and put them in movement. This causes a chain effect and creates regions of high and low pressure. Thus sound waves are generated by the interchange between the high and the low pressure. The mechanical effect is applied to sense these waves of sound on the membrane. An example in the real world I a trumpet playing. The initial sound may be a wave from one pulse, noise, continuous frequency, or a mechanical vibration. Audio sensors include the sensors responsible for changing sound waves to signals and the designed output actuators that change the signals of electricity to sound. Sound sensors can transmit and detect sound waves from low frequencies to relatively high frequencies. Sound sensors detect mechanical or acoustic waves that are produced in the human body. During this propagation, the sound wave, the characteristics of this propagation change, and this have an impact on the amplitude of the wave (Wang et al., 2013). Measuring the characteristics of the frequency of the signals depict the changes that have occurred in the velocity initially correlated to the quantity being measured.
The physics of wave explains the sound generation process, reception, and traveling, where the waves carry the disturbance from two positions. The interaction of particles causes sound traveling, which causes the waves to be transferred from one region to another. A receiver is required for a mechanical sound wave. The most commonly used biomedical technique is the EMA (Electromagnetic-acoustic) which will further elaborate on the technologies. It combines the merits of EM wave-based and the acoustic wave-based like deep penetration. These EMA technologies are depicted to provide advanced imaging compared to traditional techniques (Wang et al., 2013). The biological tissues are heated and radiated using a higher power field of EM. The elevation of the localized temperature causes the tissues to expand and then it emits ultrasonic waves.
4.2 Acoustic Sensors
Acoustic biosensors use acoustic or mechanical waves to attain biophysical, medical, and biochemical information. It also senses changes in mass, conductivity, elasticity, and dielectric properties from mechanical or electrical variations. The piezoelectric effect is used at the transducer to electrically stimulate the desired acoustic waves to gain the waves as output at the transducer (Gessner et al., 2013). Biosensors are instilled with crystals of piezoelectric like lithium niobate, quartz, or lithium tantalite that can detect biomolecules. When the medium undergoes compressions and expansions sound is generated at specific frequencies. A stethoscope instrument is used for listening and auscultation. Acoustic sensors are efficient and useful in various applications. This section introduces the piezoelectric effect and the design of the acoustic sensor and also the acoustic stethoscope.
4.3 Piezoelectric Effect
Piezoelectricity Effect is applied in detecting sounds and other electronic high voltage generation. The positive and negative are divided I crystals of piezoelectric causing the crystal to be neutral (Gessner et al., 2013). The symmetry is initially disturbed by applying stress to the material to produce voltage from the symmetry of the charge. The material of piezoelectric can be divided in accordance to the cutting procedures which are, longitudinal, transverse, and shear. General charges are independent due to shear effect and the charge is calculated using the following formula.
Cs = 2dxx Ax m
Where m is the number of elements electrically in parallel and also mechanically in series. X is the direction of the force. The dxx represents the coefficient of piezoelectric where Ax is the longitudinal effect applied. Due to the transverse effect, the force applied along the y-axis is responsible for transferring charges along the x-axis perpendicular to the force line. The transverse effect is calculated by the following equation.
Cx = dxy Ay a l b
Due to longitudinal effect, the charge displaced is independent of the shape/size of piezoelectric elements proportional to the forces applied. It is calculated by the following equation.
Cs = dxx Ax m
Application of Biomedical Acoustic Technologies
This paper evaluates the importance of cochlear implant (CI) concerning emotion perception of people differing in age, in comparison to people with normal hearing and people using hearing aids. It was on how these individuals perceive sadness, anger, happiness, disgust, and fear. The content of their emotions was placed at a similar neural sentence. The results showed better identification of auditory capabilities by these people with normal hearing and the other people with hearing problems and use hearing aids. The results were similar for people with hearing loss. However, auditory-visual-perception was much better than the visual-only for people with normal hearing, no such difference was depicted among the people with hearing problems.
The languages people use comprises of linguistic information like syntactic patterns and lexical items, and also vocal nonverbal information like the emotional state of a speaker concerning the topic being discussed. Both nonverbal information and linguistic are important for familiarizing with social interaction in our environment. The understanding of the emotional state of the speaker is based on both auditory and visual signals. The emotional state of the speaker was accurately detected by people with normal hearing (NH) based solely on auditory indications. Anger, for example, is characterized by a high average basic frequency, a wide range of basic frequencies, and an average intensity and intensity changes along the utterance; the distribution of energy in the spectral range, specifically the ratio between high frequency and low-frequency energy; the location of the formant; and finally, the duration of production or speaking. The most important contribution to emotional perception was the fundamental change in frequency along with the utterance, after which the duration and, lastly, the intensity of the utterance. Previous research identified large variations in the correct auditory experience of the various emotions. In general, it was found that rage and sorrow were easier to identify, while shock and disgust were hard to perceive. Fear, rage, and sorrow can best be recognized through the eyes, whereas joy and disgust are best recognized through the mouth. Surprise is profoundly identified through the eyes or the mouth. The precise representation of emotions through the visual mode also has a great variance. Previous research revealed that the most realistic definition of happiness is possible (Coté et al., 2003). Emotions such as panic, disgust, and discomfort were hard to identify, while frustration and sorrow were between them. Comparing the perception of feelings separately through each perceptual mode, it was noticed that emotions are better perceived through the visual mode compared to the perceived degree of the auditory mode. Nonetheless, auditory signals are very relevant because they can provide data when behavioral information is not available.
Cumulative auditory and visual signals have always led to a better understanding of emotions relative to the interpretation of auditory information alone.
As a result of partial deafness, most people may have difficulty in typically sensing the spoken sound, as well as challenges in perceiving nonverbal auditory emotional stimuli. Problems in interpreting details about the speaker's emotional state may result in a lack of comprehension of the effect of the person on others, absence of empathy and social skills that may not be adjusted to the situation. Almost all of the acoustic information regarding emotions is in the low-frequency range, and there is residual hearing in this range in many persons with hearing loss (Coté et al., 2003). However, sensorineural hearing loss harms psychoacoustic capabilities including frequency aspect ratio, frequency prejudice or time resolution, which are essential to correctly interpret emotional information. All the people experiencing hearing loss who took part in this study earned low auditory scores in psychological perception, with no correlation to the level of hearing loss. Nevertheless, it should be stated that the people in this study experienced severe and profound hearing loss, while the others had a wide range of hearing loss from moderately severe to deafness.
Past studies of people with hearing loss perceiving feelings through the visual mode generated different results. Although some found similar performance to that of people with NH. Many research findings showed no significant correlation between the level of hearing loss and the ability to analyze facial expressions accurately. The poor output of people with hearing loss when interpreting auditory emotional information was attributed to the fact that the capacity to sense emotions evolves within the spoken language context (De-An et al., 2007). The resulting emotion can be more easily identified when the child sees facial expression when hearing similar vocalization. The advancement of social understanding and social skills and the accumulation of mind theory in an infant with hearing loss who has not been subjected to a rich biological spoken language could be delayed (De-An et al., 2007). Another reason may be that individuals who interact in the spoken language may concentrate on the mouth for lip-reading and may, therefore, miss certain information on the eyes that may be important to emotions.
Unlike those with NH, people with hearing loss showed a good perception of emotions via the visual mode and the integrated auditory-visual mode than interpretation via the auditory mode. Like hearing individuals, however, the rate of emotion sensed by people with hearing loss via the combined auditory-visual feature did not exceed that perceived by emotions (De-An et al., 2007). Generally, the addition of auditory information to the visual mode did not benefit them.The section above focuses on the experience of emotion by people with hearing loss who used HAs.
Cochlear implant (CI) technology has provided treatment options for people with serious and deep hearing loss to use spoken language. Use of the CI has shown that it improves the audibility of the communication signal and thus enables a better perception of speech by kids who use CI in comparison to those who have significant loss of hearing but who use Has (Bai et al., 2012). The various studies document a significant difference in the performance of a person as a result of various variables, such as implantation age.Previous studies identified large variations in the accurate interpretation of various emotions in people. In general, it was found that rage and grief were easier to identify, while surprise and anger were hard to perceive. Nonetheless, most such works documented the understanding of speech's segmental characteristics.
Work on the understanding of suprasegmentally characteristics has been given much less focus, and the findings obtained have not generally favored the CI. For example, a 1990 research found that children with Nucleus 22-channel CI and others with HAs participated well in interpreting word focus and sound changes (Bai et al., 2012). It stated that the evaluation of syllable quantities by group using Nucleus CI did not change significantly from children with HAs and that children with HAs had better perception. Researchers clarified the poor performance of the CI in sensing suprasegmentally characteristics by proposing that the implant doesn't provide adequate information on essential frequency shifts, which are a significant acoustic predictor for the detection of suprasegmentally characteristics. Just like with the suprasegmentally characteristics, the auditory experience of the psychological state is taken care of along the speech by adjustments in the fundamental frequency as well as by the length and intensity indications. Given the significance of emotional message interpretation for effective interaction, this effect was tested by individuals with CI only by a few research studies.
In summary, in social interaction, the ability to interpret a speaker's emotional state is very significant. Because people with severe and deep hearing loss miss much of the verbal subject matter, during interaction they focus primarily on nonverbal signs (Bai et al., 2012). Previous studies identified problems in persons with hearing loss in the auditory processing of emotions. Only a few research of people using CI were conducted, and these explored the auditory experience of post-lingual deaf adolescents. Such research compared their results to the results of hearing people, but no correlation was made to people wearing HAs. Therefore, no comparison was made concerning perception through the various sensory modes. This paper aimed at examining the interpretation of emotions by individuals using CI (inserted at different ages) versus persons with HAs and persons with NH. Only the acoustic mode, the visual mode, and the mixed acoustic-visual mode tested the perception of emotion.
One approach to conceptualize the criteria for hearing aid processing would be as a ' ' hearing aid with cognition-driven signal processing, '' in which the processing of hearing aids is built to take into account human cognitive capacity to maximize speech comprehension.Creating such a cognition-driven hearing aid needs real-time monitoring of the person '' cognitive workload'' to determine the degree at which the hearing situation begins to challenge WM (working memory) resources. Based on the listening situation, WM resources are tested differently, and different people may have various cognitive resources available to achieve success. Therefore, monitoring methods need to be developed to estimate cognitive workload.This paper examined adolescents ' ability to sense nonverbal emotional resonance with and without hearing problems via an auditory mode, via a visual mode alone, and a mixed auditory-visual mode. The results of adults using HA was contrasted with that of two categories of adults using CI — those who were implanted early and those who were implanted later. The first finding confirmed that individuals with NH would have a higher auditory emotion sensitivity than all participants with hearing loss. Future research would do better to examine these inconsistencies in further detail, perhaps by using a more sensitive metric like response time to identify probable subtle distinctions in the ability to sense emotions via the visual mode. Since the method of interaction is very complex, it may be showing how long it takes to recognize a facial expression in a real-life everyday situation. Besides the difference in between the visual and the total combined modes was slightly smaller compared to the difference between the auditory and visual and the combined modes, study participants with NH were also fully able to use the same auditory information and gain even from the auditory information received in the mixed-mode concerning emotions (Ainslie & Leighton, 2009). The auditory detection of emotions for NH participants was considerably lower than visual identification or cumulative auditory-visual identification like found in previous research. Respondents with NH reliability via the combined mode significantly exceeded that of each sensory perception mode alone.
5. Conclusion
Unlike the NH subjects, who responded considerably better in the mixed acoustic-visual mode relative to any of the modes alone displayed significantly higher representations of emotions via the visual mode compared to the auditory mode, all groups of respondents with hearing loss initially performed well in the cumulative auditory-visual mode. In other words, subjects with hearing loss did not gain from the extra auditory information given in the mixed-mode, whether they used HA or CI. Excluding small differences in the levels of fear and anger, the HA group showed a consistent pattern of outcomes. Such results could not be taken into account by the acoustic research conducted on the stimuli. Concerning the acoustic metrics of the profound frequency, the intensity and the period of the stimulus, data on the sound quality of the speaker may be required. In general, focusing on the visual and acoustic sensory tests, it can be concluded that the mental state of the speaker is intelligible to people with hearing loss in many social experiences where the recipient not only sees the face of the speaker but also reacts to him / her. Even so, in situations where visual information is insufficient or unavailable, such as telephone communication (Ainslie &Leighton, 2009). This study's findings challenge the advantages of CI in transferring the acoustic information needed to identify the emotional state of the speaker. The current results highlight the effectiveness of incorporating nonverbal elements of communication, such as psychological perception, into the process of intervention for people with hearing loss using HA or CI. Close attention should be given to acoustic perception without visual input.
6. Recommendations
Studies should continue to investigate the results of existing CI coding techniques, which appear to provide better information about the ability to perceive emotions in the low-frequency range. For example, Med-El's recent strategy for fine structure processing (FSP) asserts to provide information on periodicity. Future studies ought to keep on examining individuals using CI on one ear and HA on the other to assess whether this bimodal approach leads to a better understanding of emotion. It is also recommended that children with bilateral CI be examined. Finally, future analysis of the perception of emotions by postlingually deaf people compared to prelingually deaf people would enable researchers to assess the impact of linguistic and auditory exposures on the perception of emotions
Reference
Ainslie, M. A., & Leighton, T. G. (2009). Near resonant bubble acoustic cross-section corrections, including examples from oceanography, volcanology, and biomedical ultrasound. The Journal of the Acoustical Society of America, 126(5), 2163-2175.
Bai, M., Du, L., Gu, J., Li, F., & Jia, X. (2012). Virtual touch tissue quantification using acoustic radiation force impulse technology: initial clinical experience with solid breast masses. Journal of ultrasound in medicine, 31(2), 289-294.
De-An, T. A., Yuan-Yuan, W. W. Q. W., & Jian-Guo, Y. U. (2007). Analysis of Acoustic Impedance in Cancellous Bone using Ultrasonic Backscatter Signal [J]. Chinese Journal of Biomedical Engineering, 4.
Coté, G. L., Lec, R. M., & Pishko, M. V. (2003). Emerging biomedical sensing technologies and their applications. IEEE Sensors Journal, 3(3), 251-266.
Gessner, R. C., Frederick, C. B., Foster, F. S., & Dayton, P. A. (2013). Acoustic angiography: a new imaging modality for assessing microvasculature architecture. Journal of Biomedical Imaging, 2013, 14.
Li, Q., He, X., Wang, Y., Liu, H., Xu, D., & Guo, F. (2013). Review of spectral imaging technology in biomedical engineering: achievements and challenges. Journal of biomedical optics, 18(10), 100901.
Sezen, A. S., Sivaramakrishnan, S., Hur, S., Rajamani, R., Robbins, W., & Nelson, B. J. (2005). Passive wireless MEMS microphones for biomedical applications. Journal of biomechanical engineering, 127(6), 1030-1034.
Shixi, L. J. L. (2006). Features and Clinical Application of Acoustic Parameters in Voice [J]. Journal of Biomedical Engineering, 4.
OU, D., YIN, T., ZHANG, S., MA, R., & LIU, Z. (2011). Experiment of acoustic signal detection on magnetoacoustic tomography. Journal of Biomedical Engineering Research, 30(4), 199-202.
Wiklund, M., Nilsson, S., & Hertz, H. M. (2001). Ultrasonic trapping in capillaries for trace-amount biomedical analysis. Journal of applied physics, 90(1), 421-426.