Catheterization is the gold standard for diagnosing PAH; however, it is a very complex, risky, and costly operation, requiring a highly expert clinician. The diagnosis process can be simplified by using the digital stethoscope (noninvasive method) instead of catheterization (invasive method).

In the past, clinicians have also used standard stethoscopes to diagnose PAH; however, this method faces limitations in detecting PAH. With current advances in science and technology, we now have digital stethoscopes that can be used to detect PAH by using machine-learning algorithms that support clinicians in their decision making.

It is necessary to filter heart sounds while preserving the waveforms of the S1 and S2 heart sounds [11]. In the literature, wavelet-based Shannon energy was the most-used method for emphasizing the S1 and S2 [11]. This method emphasizes medium-intensity signals and attenuates the effect of low-energy signals much more than that of high-intensity signals. Liang et al [27] first recommended the use of Shannon energy after comparing its performance to the Shannon entropy, absolute value, and energy on heart sounds.

Kumar et al [28] tried to improve Method I by introducing multiple wavelet coefficients and a duration-based threshold. Wang et al [29] found that Method I was sensitive to noise and heart murmurs, which easily leads to false segmentation. Therefore, they investigated different wavelet features and introduced a higher-order Shannon energy, specifically third order, to emphasize the S1 and S2 and to suppress the noise and murmurs.

Another mining method that can be used is based on the sequential wavelet analysis introduced by Zhong and Scalzo [30]. They developed algorithms based on the Daubechies db5 wavelet, not the db6 used in Methods I, II, and III.

Features extracted from heart sounds, such as relative power and sinusoids, have been investigated recently, and the literature has reported on the correlations between them and the detection of PAH [31,32].

The algorithm in [31] used the relative power and achieved an SE of 79% and SP of 77% over 27 subjects (12 males) with a median age of 7 years (range 3 months-19 years) undergoing simultaneous cardiac catheterization. Thirteen subjects had a mean pulmonary artery pressure (mPAp) <25 mmHg (range 8-24 mmHg). Fourteen subjects had mPAp ≥25 mmHg (range 25-97 mmHg). The results were acceptable.

The algorithm in [32] used the entropy of the first sinusoid formant and achieved sensitivity of 93% and specificity of 92% over the same data used in [31]. The results of [32] were more reliable than the results in [31].

Note, the data in [31,32] were collected in clinical settings and analyzed offline on a laptop computer, not a mobile phone. Having said that, these two studies can be considered as proof-of-concept studies for a BSP-based POC implementation.

Digital stethoscopes can be quite expensive, costing at least US $450 per unit in developed countries, and the cost is even higher in LMICs. Making it even more costly is the additional expense of a computer to analyze the heart sounds recorded by the digital stethoscope.

Locally made digital stethoscopes are an inexpensive alternative that are manufactured with available parts. Designing an inexpensive digital stethoscope consists of three components. The first component is the chest piece, which is placed on the skin to capture the heart sounds. The second component is the electret microphone, which records the heart sounds captured by the chest piece. The third component is the transmitter that sends the recorded heart sounds to a device where heart sounds can be visualized and played back for diagnosis. The following are three examples of inexpensive digital stethoscopes:

1. Hands-free kit, eggcup, and rubber O-ring. In 2010, Kuan [33] developed an inexpensive digital stethoscope that can be made for a maximum of US $40 in low volumes. The first component is a combination of a rubber gasket (US $1), a soup ladle (US $1), a plastic folder (US $1), and a hand towel (US $1). The second and third components (the electret microphone and the transmitter) are a hands-free headset (ranging from US $20-$35 per device), which captures the heart sounds and then transmits them via the headset cable to the mobile phone.

2. Mobile stethoscope. Fletcher and Chamberlain [34] developed a simple mobile stethoscope in 2015 that can be made for a maximum of US $50 in low volumes. The first component of this system is the chest piece of the traditional nondigital stethoscope (US $22). The second and third components (the electret microphone and the transmitter) are a hands-free headset (US$27) that captures and transmits heart sounds to a mobile phone.

3. Microphone with wireless kit. In 2012, Sangasoongsong et al [35] developed a wireless sensor platform that can be made for US $13 in large volumes. The first and second components of this digital stethoscope are the phonocardiography sensor and its wire (US $4). About 70% of the unit cost comes from the third component, which consists of a microprocessor (US $5) and a Zigbee transceiver chip (US $4).

Creating alternatives to the expensive digital stethoscope to capture heart sounds is a plausible and sustainable solution. Moreover, the development of these inexpensive digital stethoscopes utilizes already-existing parts for the devices, making them easily accessible and relatively more affordable.

The digital stethoscope is widely used by clinicians all over the globe. One of the main advantages of the digital stethoscope is that it can be used inside and outside any clinical setting.

In developing a robust algorithm to detect PAH that accompanies the digital stethoscope, there needs to be a graphical user interface that allows clinicians with varying backgrounds and knowledge levels to easily interact with it.

Diagnostic tests to predict individuals susceptible to heat stress include assessment of maximal aerobic power and/or heat tolerance in controlled settings [42,51]. These tests require specialized equipment and a controlled climate while inducing high levels of physiological strain. Physiological monitoring of BCT and HRV also can require specialized equipment; they may suffer from invasiveness of measurement and complex analysis, respectively.

Alternatively, PPG signal collection is a simple-to-measure and noninvasive test that can be conducted on a fingertip during scheduled breaks at work. Recent improvements in wearable sensor technology allow for the continuous measurement of PPG for the purpose of measuring HR. Information derived from the PPG signal can be analyzed to provide additional insight into physiological strain, heat stress, and autonomic arousal. While HRV standards of heat stress have yet to be developed, heat stress analysis techniques have been determined [52].

The bandpass filter is used as an essential mining step that preserves the saliency of the systolic and diastolic waves as well as the dicrotic notch. Researchers have recommended a zero-phase second-order Butterworth filter, with bandpass 0.5-8 Hz, to remove the baseline wander and high frequencies [53].

Recent investigations to detect systolic peaks in PPG signals measured after exercise reflected challenges due to motion artifacts, sweat, and nonstationary effects [53]. Studies have examined several filters and algorithms to analyze the PPG wave contour; however, they continue to lack accuracy and reproducibility [54]. As a result of these challenges, researchers have started to apply the second derivative to emphasize and easily quantify the delicate changes in the PPG contour [55]. For these reasons, a second derivative is used to improve the mining and increase accuracy.

There has been a recent attempt to connect PPG features to the effect of heat stress while investigating global warming [52]. We have examined existing PPG features used in the literature to diagnose different diseases, such as the b/a index, the amplitude of the a wave, and the amplitude of the b wave in the acceleration photoplethysmogram (APG). Furthermore, we tested new features to determine the optimal PPG feature for heat stress detection, such as the energy of the aa area, the energy of the ab area, the energy of the ba area, and the slope of the ab segment. In total, we tested 14 time-domain features—seven features extracted from the PPG signals and seven features extracted from the APG signals [52].

The algorithm in [52] used the combination of entropy and HRV index and achieved a sensitivity of 95% and positive predictivity of 90.48% on 40 healthy, heat-acclimatized emergency responders (30 males and 10 females) with a median age of 34 years. The participants were normotensive (mean systolic blood pressure of 129.3 mmHg, range 110-165 mmHg) and had no known cardiovascular, neurological, or respiratory disease. The range of systolic blood pressure exceeds the usual normotensive range. The results were considered reliable and more robust against the existing method.

Note, the data in [52] were collected in the in Australia as part of the National Critical Care and Trauma Response Centre project to assess the physiological and perceptual responses of emergency responders to simulated chemical, biological, and radiological incidents in tropical environmental conditions to compare the efficacy of various cooling methods.

The PPG signals were collected in a very noisy setting and were analyzed offline on a laptop computer, not a mobile phone. Given the challenging environment, the work is considered promising for BSP-based POC implementation.

Current PPG devices in developing countries are quite costly at around US $300 per unit [56]. An affordable alternative solution is converting a mobile phone into a BSP-based POC device. The comparative cost for a mobile phone in some developing countries is approximately US $15, and the addition of the PPG costs approximately only US $3 more. Clearly, the option of using the mobile phone is very promising in terms of affordability when compared to stand-alone PPG devices.

In the developed world, the use of the PPG signal for anesthesia monitoring during surgery has been the standard of care for more than 20 years. The WHO is now leading the Global Pulse Oximetry Project, which aims to make the PPG component available in every operating room in the world [57].

A minimum amount of knowledge is needed to use the device as the PPG probe is very intuitive. The user simply places the clip on the patient’s finger to collect the PPG signal. The software will show the PPG signal in real time along with the automatic diagnosis.

To diagnosis preeclampsia, both hypertension and proteinuria must be present [60]. Therefore, there is a need for two items. First, a blood pressure cuff is needed to check if blood pressure is ≥140 mm Hg (systolic), or ≥90 mm Hg (diastolic) after 20 weeks of gestation in a woman with previously normal blood pressure. The second component is a urine test to check if proteinuria is ≥0.3 g of protein in a 24-hour urine collection [60]. These two tests are usually unavailable in developing countries; therefore, there is a need to improve (or replace) current PE diagnosis with a simple method, such as PPG signals.

Oxygen saturation (SpO2) is related to hypertension [61], and therefore, the use of pulse oximeter can be a simpler way to improve the diagnosis/detection of PE and its related complications.

This mining step is similar to the mining step discussed in Case II.

Oxygen saturation (SpO2) is related to hypertension [61]; therefore, we use it to calculate the risk prediction index. The light transmitted from the light-emitting diode (LED) in the PPG probe can be detected on the same side (reflectance mode) or on the other side (transmittance mode) of the tissue by a photodetector. The output from the photodetector is converted into a voltage and then further processed producing PPG [62]. The signal can be divided into a pulsatile (alternating current [ac]) and a relatively constant (direct current [dc]) PPG component. The SpO2 then is calculated using the ratios of the ac and dc components of the red and infrared PPG signals along with a calibration curve [63]. The SpO2 values then are calculated every 10 seconds ac, and the dc PPG amplitudes are determined using the empirically calibrated equation [63,64].

Recently, the use of SpO2 has been tested as part of the miniPIERS prediction model [65] in a proof-of-concept study including a cohort of 726 women (118 of whom had adverse pregnancy outcomes) in South Africa and Pakistan. These women were admitted into hospitals with suspected or confirmed PE (ie, with any hypertensive disorder of pregnancy). Interestingly, the preliminary results showed that adding oxygen saturation derived from the PPG signals improved prediction accuracy from 81% to 84% [61].

The PPG signals were collected using a mobile phone with a real-time analysis capability and can be viewed as a real-world practical implementation for BSP-based POC devices.

The affordability step is similar to the affordability step discussed in Case II.

The scalability step is similar to the scalability step discussed in Case II.

Researchers have put forward EEG as a potential low-cost diagnostic tool in the early stages of AD. Compared to other systems like functional magnetic resonance imaging or positron emission tomography, EEG systems are simple, easy to use, and cost efficient.

Until now, most technological solutions addressing AD have focused on the satisfaction of a specific need, such as position tracking, memory and skill enhancement, and daily needs reminders [69,70]. Neural feedback may improve the user’s (or patient’s) ability to control brain activity, help with the diagnosis of medical conditions, and assist in the rehabilitation of neurological or psychiatric disorders. Several psychological and medical studies have confirmed that neurofeedback activity is enjoyable, stimulating, and potentially healing. Neurofeedback is generated from the EEG signals of AD patients and healthy subjects. The auditory and visual representations of AD EEG differ substantially from healthy EEGs, potentially yielding novel diagnostic tools. Moreover, such alternative representations of AD EEG are natural and intuitive, making them easily accessible to laypeople (AD patients and family members) while providing insight into the abnormal brainwaves associated with AD.

Researchers recently developed a simple neurofeedback methodology that uses real-time collection of EEG signals with a wireless EEG headset, specifically the Emotiv EPOC wireless headset, with a sampling frequency of 128 Hz. The headset has 14 data-collecting electrodes and two reference electrodes. The electrodes are placed at 10-20 locations, AF3/4, F3/4, FC5/6, F7/8, T7/8, P7/8, and O1/2. The BCI2000 software package [71] was used to interface with the Emotiv EPOC wireless headset. The headset transmits encrypted data wirelessly to a laptop computer.

Low-cost EEG headsets, such as Emotiv (14 electrodes) and OpenBCI (16 electrodes) were originally designed for entertainment purposes (eg, video games) [72]; however, these devices seem to be prone to various artifacts, such as eye blinking, ECG, electromyogram (EMG), body movements, and power sources. These artifacts easily obscure the EEG signal and make analysis difficult. Currently, a study has been proposed to combine the gyroscope with EEG signals to optimally remove artifacts from EEG signals collected using wireless EEG headsets [73].

EEG signals are corrupted by noise and artifacts: 50/60 Hz powerline interference, motion, eye-blinking artifacts, EMG signals from muscles, and artifacts due to changes in the electrode-skin interface [74]. The gamma range (30-100 Hz) has a particularly low signal-to-noise ratio, and researchers exclude it from further analysis. Therefore, the frequency range of investigation is 4-30 Hz [75].

The literature has reported a strong relationship between the slowing of EEG and AD. The results presented in [12] demonstrate that relative power, specifically within the 4-10 Hz band, holds discriminative features to detect AD.

Researchers consistently found and confirmed that AD is associated with the slowing of EEG (frequency reduction in the power spectrum density) [12]. There is also a reduced overall synchrony [76] between EEG leads when compared to healthy subjects.

Connecting the relative power features with the classification of AD using sonification is proposed in [13]. The system computes the relative power features (f1, f2, f3) in three nonoverlapping frequency bands (4-10 Hz, 10-20 Hz, and 20-30 Hz).

The EEG sonification system then generates melody notes from the computed values depending on whether the values are above or below a predetermined threshold. To prove the concept, we used notes from only one octave (MIDI Octave -1) with the pentatonic scale (five notes per octave), and the study was limited to only one instrument (acoustic bass). Obviously, it is possible to incorporate additional musical instruments and multiple octaves. However, the extracted sound easily becomes cacophonic and difficult to parse. In the future, there is a need to explore alternative schemes to generate music from EEG relative power and other EEG patterns in the time-frequency domain.

As a proof-of-concept study [77], two databases were used. One contained mild cognitive impairment (MCI) and healthy subjects (patient age 71.9, SD 10.2; healthy subject age 71.7, SD 8.3), and the other contained mild AD and healthy subjects (patient age 77.6, SD 10.0; healthy subject age 69.4, SD 11.5). The use of a single feature achieved a detection rate of 78.33% for the MCI dataset and 97.56% for mild AD. When multiple features were used, the detection rate improved. More specifically, 11 features achieved 95% in the MCI dataset, and four features achieved 100% in the mild AD dataset. The results were very promising and were considered reliable.

The EEG signals were collected in a clinical setting; however, the data analysis was applied in real-time using a portable laptop, not a mobile phone. This work is considered promising for BSP-based POC devices.

EEG systems are relatively inexpensive; with suitable signal processing, they may become useful for research and clinical purposes. In developing countries, several EEG headsets are affordable while the computer/phone accompanying the headset is relatively costly.

EEG systems are easy to use and commonly utilized by neurologists all over the world. Minimal knowledge is needed to use the EEG device.