Coccygodynia (coccydynia) is a painful condition of the perineum in the region of the tailbone or coccyx, aggravated by sitting on hard surfaces. It is frequently associated with injuries to the coccyx following direct trauma. Nevertheless, idiopathic coccygodynia without antecedent trauma history is not uncommon. Most of these patients respond to anti-inflammatory medications and physical therapy. Those who are unresponsive may require additional intervention for pain relief. Blockade of ganglion impar, the terminal end of the pelvic sympathetic chain, can dramatically alleviate the pain in patients suffering from coccygodynia. In the current case series, four patients in the age range of 21 to 69 years suffering from chronic idiopathic coccygodynia (female: male ratio of 1:1) were treated with ganglion impar block. All four patients received a course of medical management, and two of the patients additionally received local infiltration of the coccyx before ganglion impar block administration. The block was performed with fluoroscopy guidance by either the trans-sacrococcygeal joint approach or the intra-coccygeal joint approach. The pre-intervention average numeric rating pain score (NRS) was 7.5. After a single ganglion impar block intervention, all four patients experienced complete pain relief (NRS=0). No patients required a repeat injection, and all were pain-free for the entire one-year follow-up period.

This paper proposes enhanced particle swarm optimization (PSO) with craziness factor based extreme learning machine (ELM) for feature classification of single and combined power quality disturbances. In the proposed method, an S-transform technique is applied for feature extraction. PSO with craziness factor is applied to adjust the input weight and hidden biases of ELM. To test the effectiveness of the proposed approach, eight possible combinations of single and combined power quality disturbances are assumed in the sampled form and the performance of the proposed approach is investigated. In addition white gaussian noise of different signal-tonoise ratio is added to the signals and the performance of the algorithm is analysed. The results indicate that the proposed approach can be effectively applied for classification of power quality disturbances.

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